{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import joblib\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>A1</th>\n",
       "      <th>Ea1</th>\n",
       "      <th>A2</th>\n",
       "      <th>Ea2</th>\n",
       "      <th>A3</th>\n",
       "      <th>Ea3</th>\n",
       "      <th>A4</th>\n",
       "      <th>Ea4</th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>cSM_360s</th>\n",
       "      <th>cSM_480s</th>\n",
       "      <th>cSM_600s</th>\n",
       "      <th>cSM_720s</th>\n",
       "      <th>cSM_840s</th>\n",
       "      <th>cSM_960s</th>\n",
       "      <th>cSM_1080s</th>\n",
       "      <th>cSM_1200s</th>\n",
       "      <th>cSM_1500s</th>\n",
       "      <th>cSM_1800s</th>\n",
       "      <th>cSM_2400s</th>\n",
       "      <th>cSM_3000s</th>\n",
       "      <th>cSM_3600s</th>\n",
       "      <th>cSM_4500s</th>\n",
       "      <th>cSM_5400s</th>\n",
       "      <th>cSM_6300s</th>\n",
       "      <th>cSM_7200s</th>\n",
       "      <th>cSM_9000s</th>\n",
       "      <th>cSM_10800s</th>\n",
       "      <th>cSM_14400s</th>\n",
       "      <th>cR_0s</th>\n",
       "      <th>cR_0.1s</th>\n",
       "      <th>cR_1s</th>\n",
       "      <th>cR_20s</th>\n",
       "      <th>cR_40s</th>\n",
       "      <th>cR_60s</th>\n",
       "      <th>cR_120s</th>\n",
       "      <th>cR_180s</th>\n",
       "      <th>cR_240s</th>\n",
       "      <th>cR_300s</th>\n",
       "      <th>cR_360s</th>\n",
       "      <th>cR_480s</th>\n",
       "      <th>cR_600s</th>\n",
       "      <th>cR_720s</th>\n",
       "      <th>cR_840s</th>\n",
       "      <th>cR_960s</th>\n",
       "      <th>cR_1080s</th>\n",
       "      <th>cR_1200s</th>\n",
       "      <th>cR_1500s</th>\n",
       "      <th>cR_1800s</th>\n",
       "      <th>cR_2400s</th>\n",
       "      <th>cR_3000s</th>\n",
       "      <th>cR_3600s</th>\n",
       "      <th>cR_4500s</th>\n",
       "      <th>cR_5400s</th>\n",
       "      <th>cR_6300s</th>\n",
       "      <th>cR_7200s</th>\n",
       "      <th>cR_9000s</th>\n",
       "      <th>cR_10800s</th>\n",
       "      <th>cR_14400s</th>\n",
       "      <th>cB_0s</th>\n",
       "      <th>cB_0.1s</th>\n",
       "      <th>cB_1s</th>\n",
       "      <th>cB_20s</th>\n",
       "      <th>cB_40s</th>\n",
       "      <th>cB_60s</th>\n",
       "      <th>cB_120s</th>\n",
       "      <th>cB_180s</th>\n",
       "      <th>cB_240s</th>\n",
       "      <th>cB_300s</th>\n",
       "      <th>cB_360s</th>\n",
       "      <th>cB_480s</th>\n",
       "      <th>cB_600s</th>\n",
       "      <th>cB_720s</th>\n",
       "      <th>cB_840s</th>\n",
       "      <th>cB_960s</th>\n",
       "      <th>cB_1080s</th>\n",
       "      <th>cB_1200s</th>\n",
       "      <th>cB_1500s</th>\n",
       "      <th>cB_1800s</th>\n",
       "      <th>cB_2400s</th>\n",
       "      <th>cB_3000s</th>\n",
       "      <th>cB_3600s</th>\n",
       "      <th>cB_4500s</th>\n",
       "      <th>cB_5400s</th>\n",
       "      <th>cB_6300s</th>\n",
       "      <th>cB_7200s</th>\n",
       "      <th>cB_9000s</th>\n",
       "      <th>cB_10800s</th>\n",
       "      <th>cB_14400s</th>\n",
       "      <th>cC_0s</th>\n",
       "      <th>cC_0.1s</th>\n",
       "      <th>cC_1s</th>\n",
       "      <th>cC_20s</th>\n",
       "      <th>cC_40s</th>\n",
       "      <th>cC_60s</th>\n",
       "      <th>cC_120s</th>\n",
       "      <th>cC_180s</th>\n",
       "      <th>cC_240s</th>\n",
       "      <th>cC_300s</th>\n",
       "      <th>cC_360s</th>\n",
       "      <th>cC_480s</th>\n",
       "      <th>cC_600s</th>\n",
       "      <th>cC_720s</th>\n",
       "      <th>cC_840s</th>\n",
       "      <th>cC_960s</th>\n",
       "      <th>cC_1080s</th>\n",
       "      <th>cC_1200s</th>\n",
       "      <th>cC_1500s</th>\n",
       "      <th>cC_1800s</th>\n",
       "      <th>cC_2400s</th>\n",
       "      <th>cC_3000s</th>\n",
       "      <th>cC_3600s</th>\n",
       "      <th>cC_4500s</th>\n",
       "      <th>cC_5400s</th>\n",
       "      <th>cC_6300s</th>\n",
       "      <th>cC_7200s</th>\n",
       "      <th>cC_9000s</th>\n",
       "      <th>cC_10800s</th>\n",
       "      <th>cC_14400s</th>\n",
       "      <th>cP_0s</th>\n",
       "      <th>cP_0.1s</th>\n",
       "      <th>cP_1s</th>\n",
       "      <th>cP_20s</th>\n",
       "      <th>cP_40s</th>\n",
       "      <th>cP_60s</th>\n",
       "      <th>cP_120s</th>\n",
       "      <th>cP_180s</th>\n",
       "      <th>cP_240s</th>\n",
       "      <th>cP_300s</th>\n",
       "      <th>cP_360s</th>\n",
       "      <th>cP_480s</th>\n",
       "      <th>cP_600s</th>\n",
       "      <th>cP_720s</th>\n",
       "      <th>cP_840s</th>\n",
       "      <th>cP_960s</th>\n",
       "      <th>cP_1080s</th>\n",
       "      <th>cP_1200s</th>\n",
       "      <th>cP_1500s</th>\n",
       "      <th>cP_1800s</th>\n",
       "      <th>cP_2400s</th>\n",
       "      <th>cP_3000s</th>\n",
       "      <th>cP_3600s</th>\n",
       "      <th>cP_4500s</th>\n",
       "      <th>cP_5400s</th>\n",
       "      <th>cP_6300s</th>\n",
       "      <th>cP_7200s</th>\n",
       "      <th>cP_9000s</th>\n",
       "      <th>cP_10800s</th>\n",
       "      <th>cP_14400s</th>\n",
       "      <th>cIMP1_0s</th>\n",
       "      <th>cIMP1_0.1s</th>\n",
       "      <th>cIMP1_1s</th>\n",
       "      <th>cIMP1_20s</th>\n",
       "      <th>cIMP1_40s</th>\n",
       "      <th>cIMP1_60s</th>\n",
       "      <th>cIMP1_120s</th>\n",
       "      <th>cIMP1_180s</th>\n",
       "      <th>cIMP1_240s</th>\n",
       "      <th>cIMP1_300s</th>\n",
       "      <th>cIMP1_360s</th>\n",
       "      <th>cIMP1_480s</th>\n",
       "      <th>cIMP1_600s</th>\n",
       "      <th>cIMP1_720s</th>\n",
       "      <th>cIMP1_840s</th>\n",
       "      <th>cIMP1_960s</th>\n",
       "      <th>cIMP1_1080s</th>\n",
       "      <th>cIMP1_1200s</th>\n",
       "      <th>cIMP1_1500s</th>\n",
       "      <th>cIMP1_1800s</th>\n",
       "      <th>cIMP1_2400s</th>\n",
       "      <th>cIMP1_3000s</th>\n",
       "      <th>cIMP1_3600s</th>\n",
       "      <th>cIMP1_4500s</th>\n",
       "      <th>cIMP1_5400s</th>\n",
       "      <th>cIMP1_6300s</th>\n",
       "      <th>cIMP1_7200s</th>\n",
       "      <th>cIMP1_9000s</th>\n",
       "      <th>cIMP1_10800s</th>\n",
       "      <th>cIMP1_14400s</th>\n",
       "      <th>cIMP2_0s</th>\n",
       "      <th>cIMP2_0.1s</th>\n",
       "      <th>cIMP2_1s</th>\n",
       "      <th>cIMP2_20s</th>\n",
       "      <th>cIMP2_40s</th>\n",
       "      <th>cIMP2_60s</th>\n",
       "      <th>cIMP2_120s</th>\n",
       "      <th>cIMP2_180s</th>\n",
       "      <th>cIMP2_240s</th>\n",
       "      <th>cIMP2_300s</th>\n",
       "      <th>cIMP2_360s</th>\n",
       "      <th>cIMP2_480s</th>\n",
       "      <th>cIMP2_600s</th>\n",
       "      <th>cIMP2_720s</th>\n",
       "      <th>cIMP2_840s</th>\n",
       "      <th>cIMP2_960s</th>\n",
       "      <th>cIMP2_1080s</th>\n",
       "      <th>cIMP2_1200s</th>\n",
       "      <th>cIMP2_1500s</th>\n",
       "      <th>cIMP2_1800s</th>\n",
       "      <th>cIMP2_2400s</th>\n",
       "      <th>cIMP2_3000s</th>\n",
       "      <th>cIMP2_3600s</th>\n",
       "      <th>cIMP2_4500s</th>\n",
       "      <th>cIMP2_5400s</th>\n",
       "      <th>cIMP2_6300s</th>\n",
       "      <th>cIMP2_7200s</th>\n",
       "      <th>cIMP2_9000s</th>\n",
       "      <th>cIMP2_10800s</th>\n",
       "      <th>cIMP2_14400s</th>\n",
       "      <th>cINT1_0s</th>\n",
       "      <th>cINT1_0.1s</th>\n",
       "      <th>cINT1_1s</th>\n",
       "      <th>cINT1_20s</th>\n",
       "      <th>cINT1_40s</th>\n",
       "      <th>cINT1_60s</th>\n",
       "      <th>cINT1_120s</th>\n",
       "      <th>cINT1_180s</th>\n",
       "      <th>cINT1_240s</th>\n",
       "      <th>cINT1_300s</th>\n",
       "      <th>cINT1_360s</th>\n",
       "      <th>cINT1_480s</th>\n",
       "      <th>cINT1_600s</th>\n",
       "      <th>cINT1_720s</th>\n",
       "      <th>cINT1_840s</th>\n",
       "      <th>cINT1_960s</th>\n",
       "      <th>cINT1_1080s</th>\n",
       "      <th>cINT1_1200s</th>\n",
       "      <th>cINT1_1500s</th>\n",
       "      <th>cINT1_1800s</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "      <th>Fast_rxn1</th>\n",
       "      <th>Medium_rxn1</th>\n",
       "      <th>Slow_rxn1</th>\n",
       "      <th>Fast_rxn2</th>\n",
       "      <th>Medium_rxn2</th>\n",
       "      <th>Slow_rxn2</th>\n",
       "      <th>Reaction_order</th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>1933581</td>\n",
       "      <td>273</td>\n",
       "      <td>261.472219</td>\n",
       "      <td>194</td>\n",
       "      <td>0.044413</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.294614</td>\n",
       "      <td>0.264231</td>\n",
       "      <td>0.182232</td>\n",
       "      <td>0.166794</td>\n",
       "      <td>0.158981</td>\n",
       "      <td>0.147680</td>\n",
       "      <td>0.142226</td>\n",
       "      <td>0.138849</td>\n",
       "      <td>0.136498</td>\n",
       "      <td>0.134742</td>\n",
       "      <td>0.132256</td>\n",
       "      <td>0.130561</td>\n",
       "      <td>0.129315</td>\n",
       "      <td>0.128356</td>\n",
       "      <td>0.127591</td>\n",
       "      <td>0.126961</td>\n",
       "      <td>0.126439</td>\n",
       "      <td>0.125435</td>\n",
       "      <td>0.124712</td>\n",
       "      <td>0.123742</td>\n",
       "      <td>0.123113</td>\n",
       "      <td>0.122668</td>\n",
       "      <td>0.122203</td>\n",
       "      <td>0.121878</td>\n",
       "      <td>0.121639</td>\n",
       "      <td>0.121454</td>\n",
       "      <td>0.121187</td>\n",
       "      <td>0.121005</td>\n",
       "      <td>0.120770</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.444614</td>\n",
       "      <td>0.414231</td>\n",
       "      <td>0.332232</td>\n",
       "      <td>0.316794</td>\n",
       "      <td>0.308981</td>\n",
       "      <td>0.297680</td>\n",
       "      <td>0.292226</td>\n",
       "      <td>0.288849</td>\n",
       "      <td>0.286498</td>\n",
       "      <td>0.284742</td>\n",
       "      <td>0.282256</td>\n",
       "      <td>0.280561</td>\n",
       "      <td>0.279315</td>\n",
       "      <td>0.278356</td>\n",
       "      <td>0.277591</td>\n",
       "      <td>0.276961</td>\n",
       "      <td>0.276439</td>\n",
       "      <td>0.275435</td>\n",
       "      <td>0.274712</td>\n",
       "      <td>0.273742</td>\n",
       "      <td>0.273113</td>\n",
       "      <td>0.272668</td>\n",
       "      <td>0.272203</td>\n",
       "      <td>0.271878</td>\n",
       "      <td>0.271639</td>\n",
       "      <td>0.271454</td>\n",
       "      <td>0.271187</td>\n",
       "      <td>0.271005</td>\n",
       "      <td>0.270770</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.897307</td>\n",
       "      <td>0.882116</td>\n",
       "      <td>0.841116</td>\n",
       "      <td>0.833397</td>\n",
       "      <td>0.829490</td>\n",
       "      <td>0.823840</td>\n",
       "      <td>0.821113</td>\n",
       "      <td>0.819425</td>\n",
       "      <td>0.818249</td>\n",
       "      <td>0.817371</td>\n",
       "      <td>0.816128</td>\n",
       "      <td>0.815280</td>\n",
       "      <td>0.814658</td>\n",
       "      <td>0.814178</td>\n",
       "      <td>0.813796</td>\n",
       "      <td>0.813481</td>\n",
       "      <td>0.813220</td>\n",
       "      <td>0.812717</td>\n",
       "      <td>0.812356</td>\n",
       "      <td>0.811871</td>\n",
       "      <td>0.811556</td>\n",
       "      <td>0.811334</td>\n",
       "      <td>0.811101</td>\n",
       "      <td>0.810939</td>\n",
       "      <td>0.810820</td>\n",
       "      <td>0.810727</td>\n",
       "      <td>0.810594</td>\n",
       "      <td>0.810503</td>\n",
       "      <td>0.810385</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.087307</td>\n",
       "      <td>0.072116</td>\n",
       "      <td>0.031116</td>\n",
       "      <td>0.023397</td>\n",
       "      <td>0.019490</td>\n",
       "      <td>0.013840</td>\n",
       "      <td>0.011113</td>\n",
       "      <td>0.009425</td>\n",
       "      <td>0.008249</td>\n",
       "      <td>0.007371</td>\n",
       "      <td>0.006128</td>\n",
       "      <td>0.005280</td>\n",
       "      <td>0.004658</td>\n",
       "      <td>0.004178</td>\n",
       "      <td>0.003796</td>\n",
       "      <td>0.003481</td>\n",
       "      <td>0.003220</td>\n",
       "      <td>0.002717</td>\n",
       "      <td>0.002356</td>\n",
       "      <td>0.001871</td>\n",
       "      <td>0.001556</td>\n",
       "      <td>0.001334</td>\n",
       "      <td>0.001101</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.150001</td>\n",
       "      <td>0.150033</td>\n",
       "      <td>0.151533</td>\n",
       "      <td>0.152891</td>\n",
       "      <td>0.154049</td>\n",
       "      <td>0.156821</td>\n",
       "      <td>0.158977</td>\n",
       "      <td>0.160764</td>\n",
       "      <td>0.162300</td>\n",
       "      <td>0.163651</td>\n",
       "      <td>0.165951</td>\n",
       "      <td>0.167870</td>\n",
       "      <td>0.169517</td>\n",
       "      <td>0.170960</td>\n",
       "      <td>0.172244</td>\n",
       "      <td>0.173401</td>\n",
       "      <td>0.174452</td>\n",
       "      <td>0.176724</td>\n",
       "      <td>0.178618</td>\n",
       "      <td>0.181647</td>\n",
       "      <td>0.184013</td>\n",
       "      <td>0.185946</td>\n",
       "      <td>0.188297</td>\n",
       "      <td>0.190198</td>\n",
       "      <td>0.191782</td>\n",
       "      <td>0.193140</td>\n",
       "      <td>0.195365</td>\n",
       "      <td>0.197137</td>\n",
       "      <td>0.199840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.693084e-03</td>\n",
       "      <td>0.017884</td>\n",
       "      <td>0.058884</td>\n",
       "      <td>0.066603</td>\n",
       "      <td>0.070510</td>\n",
       "      <td>0.076160</td>\n",
       "      <td>0.078887</td>\n",
       "      <td>0.080575</td>\n",
       "      <td>0.081751</td>\n",
       "      <td>0.082629</td>\n",
       "      <td>0.083872</td>\n",
       "      <td>0.084720</td>\n",
       "      <td>0.085342</td>\n",
       "      <td>0.085822</td>\n",
       "      <td>0.086204</td>\n",
       "      <td>0.086519</td>\n",
       "      <td>0.086780</td>\n",
       "      <td>0.087283</td>\n",
       "      <td>0.087644</td>\n",
       "      <td>0.088129</td>\n",
       "      <td>0.088444</td>\n",
       "      <td>0.088666</td>\n",
       "      <td>0.088899</td>\n",
       "      <td>0.089061</td>\n",
       "      <td>0.089180</td>\n",
       "      <td>0.089273</td>\n",
       "      <td>0.089406</td>\n",
       "      <td>0.089497</td>\n",
       "      <td>0.089615</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.692579e-03</td>\n",
       "      <td>0.017851</td>\n",
       "      <td>0.057351</td>\n",
       "      <td>0.063712</td>\n",
       "      <td>0.066461</td>\n",
       "      <td>0.069339</td>\n",
       "      <td>0.069910</td>\n",
       "      <td>0.069811</td>\n",
       "      <td>0.069451</td>\n",
       "      <td>0.068978</td>\n",
       "      <td>0.067921</td>\n",
       "      <td>0.066850</td>\n",
       "      <td>0.065826</td>\n",
       "      <td>0.064862</td>\n",
       "      <td>6.396068e-02</td>\n",
       "      <td>6.311790e-02</td>\n",
       "      <td>6.232886e-02</td>\n",
       "      <td>6.055913e-02</td>\n",
       "      <td>5.902615e-02</td>\n",
       "      <td>5.648263e-02</td>\n",
       "      <td>5.443118e-02</td>\n",
       "      <td>5.271993e-02</td>\n",
       "      <td>5.060185e-02</td>\n",
       "      <td>4.886322e-02</td>\n",
       "      <td>4.739870e-02</td>\n",
       "      <td>4.613309e-02</td>\n",
       "      <td>4.404100e-02</td>\n",
       "      <td>4.236088e-02</td>\n",
       "      <td>3.977472e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>1933582</td>\n",
       "      <td>273</td>\n",
       "      <td>596.074861</td>\n",
       "      <td>62</td>\n",
       "      <td>8.957436</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.288006</td>\n",
       "      <td>0.241010</td>\n",
       "      <td>0.163040</td>\n",
       "      <td>0.150934</td>\n",
       "      <td>0.145032</td>\n",
       "      <td>0.136843</td>\n",
       "      <td>0.133071</td>\n",
       "      <td>0.130807</td>\n",
       "      <td>0.129271</td>\n",
       "      <td>0.128149</td>\n",
       "      <td>0.126603</td>\n",
       "      <td>0.125576</td>\n",
       "      <td>0.124838</td>\n",
       "      <td>0.124282</td>\n",
       "      <td>0.123845</td>\n",
       "      <td>0.123492</td>\n",
       "      <td>0.123199</td>\n",
       "      <td>0.122650</td>\n",
       "      <td>0.122268</td>\n",
       "      <td>0.121763</td>\n",
       "      <td>0.121446</td>\n",
       "      <td>0.121221</td>\n",
       "      <td>0.120997</td>\n",
       "      <td>0.120839</td>\n",
       "      <td>0.120729</td>\n",
       "      <td>0.120638</td>\n",
       "      <td>0.120521</td>\n",
       "      <td>0.120422</td>\n",
       "      <td>0.120333</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.438006</td>\n",
       "      <td>0.391010</td>\n",
       "      <td>0.313040</td>\n",
       "      <td>0.300934</td>\n",
       "      <td>0.295032</td>\n",
       "      <td>0.286843</td>\n",
       "      <td>0.283071</td>\n",
       "      <td>0.280807</td>\n",
       "      <td>0.279271</td>\n",
       "      <td>0.278149</td>\n",
       "      <td>0.276603</td>\n",
       "      <td>0.275576</td>\n",
       "      <td>0.274838</td>\n",
       "      <td>0.274282</td>\n",
       "      <td>0.273845</td>\n",
       "      <td>0.273492</td>\n",
       "      <td>0.273199</td>\n",
       "      <td>0.272650</td>\n",
       "      <td>0.272268</td>\n",
       "      <td>0.271763</td>\n",
       "      <td>0.271446</td>\n",
       "      <td>0.271221</td>\n",
       "      <td>0.270997</td>\n",
       "      <td>0.270839</td>\n",
       "      <td>0.270729</td>\n",
       "      <td>0.270638</td>\n",
       "      <td>0.270521</td>\n",
       "      <td>0.270422</td>\n",
       "      <td>0.270333</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.894003</td>\n",
       "      <td>0.870505</td>\n",
       "      <td>0.831520</td>\n",
       "      <td>0.825467</td>\n",
       "      <td>0.822516</td>\n",
       "      <td>0.818421</td>\n",
       "      <td>0.816535</td>\n",
       "      <td>0.815403</td>\n",
       "      <td>0.814636</td>\n",
       "      <td>0.814074</td>\n",
       "      <td>0.813302</td>\n",
       "      <td>0.812788</td>\n",
       "      <td>0.812419</td>\n",
       "      <td>0.812141</td>\n",
       "      <td>0.811922</td>\n",
       "      <td>0.811746</td>\n",
       "      <td>0.811599</td>\n",
       "      <td>0.811325</td>\n",
       "      <td>0.811134</td>\n",
       "      <td>0.810881</td>\n",
       "      <td>0.810723</td>\n",
       "      <td>0.810611</td>\n",
       "      <td>0.810498</td>\n",
       "      <td>0.810420</td>\n",
       "      <td>0.810364</td>\n",
       "      <td>0.810319</td>\n",
       "      <td>0.810260</td>\n",
       "      <td>0.810211</td>\n",
       "      <td>0.810166</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.084003</td>\n",
       "      <td>0.060505</td>\n",
       "      <td>0.021520</td>\n",
       "      <td>0.015467</td>\n",
       "      <td>0.012516</td>\n",
       "      <td>0.008421</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>0.005403</td>\n",
       "      <td>0.004636</td>\n",
       "      <td>0.004074</td>\n",
       "      <td>0.003302</td>\n",
       "      <td>0.002788</td>\n",
       "      <td>0.002419</td>\n",
       "      <td>0.002141</td>\n",
       "      <td>0.001922</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.001599</td>\n",
       "      <td>0.001325</td>\n",
       "      <td>0.001134</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.000723</td>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.150234</td>\n",
       "      <td>0.158998</td>\n",
       "      <td>0.214963</td>\n",
       "      <td>0.222816</td>\n",
       "      <td>0.226315</td>\n",
       "      <td>0.230952</td>\n",
       "      <td>0.233026</td>\n",
       "      <td>0.234256</td>\n",
       "      <td>0.235084</td>\n",
       "      <td>0.235687</td>\n",
       "      <td>0.236513</td>\n",
       "      <td>0.237059</td>\n",
       "      <td>0.237451</td>\n",
       "      <td>0.237746</td>\n",
       "      <td>0.237978</td>\n",
       "      <td>0.238164</td>\n",
       "      <td>0.238319</td>\n",
       "      <td>0.238608</td>\n",
       "      <td>0.238809</td>\n",
       "      <td>0.239075</td>\n",
       "      <td>0.239242</td>\n",
       "      <td>0.239360</td>\n",
       "      <td>0.239477</td>\n",
       "      <td>0.239560</td>\n",
       "      <td>0.239618</td>\n",
       "      <td>0.239666</td>\n",
       "      <td>0.239727</td>\n",
       "      <td>0.239779</td>\n",
       "      <td>0.239826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.997070e-03</td>\n",
       "      <td>0.029495</td>\n",
       "      <td>0.068480</td>\n",
       "      <td>0.074533</td>\n",
       "      <td>0.077484</td>\n",
       "      <td>0.081579</td>\n",
       "      <td>0.083465</td>\n",
       "      <td>0.084597</td>\n",
       "      <td>0.085364</td>\n",
       "      <td>0.085926</td>\n",
       "      <td>0.086698</td>\n",
       "      <td>0.087212</td>\n",
       "      <td>0.087581</td>\n",
       "      <td>0.087859</td>\n",
       "      <td>0.088078</td>\n",
       "      <td>0.088254</td>\n",
       "      <td>0.088401</td>\n",
       "      <td>0.088675</td>\n",
       "      <td>0.088866</td>\n",
       "      <td>0.089119</td>\n",
       "      <td>0.089277</td>\n",
       "      <td>0.089389</td>\n",
       "      <td>0.089502</td>\n",
       "      <td>0.089580</td>\n",
       "      <td>0.089636</td>\n",
       "      <td>0.089681</td>\n",
       "      <td>0.089740</td>\n",
       "      <td>0.089789</td>\n",
       "      <td>0.089834</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.762906e-03</td>\n",
       "      <td>0.020497</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>0.000626</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.000341</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>1.001867e-04</td>\n",
       "      <td>9.011584e-05</td>\n",
       "      <td>8.187918e-05</td>\n",
       "      <td>6.681850e-05</td>\n",
       "      <td>5.660047e-05</td>\n",
       "      <td>4.337807e-05</td>\n",
       "      <td>3.526312e-05</td>\n",
       "      <td>2.960174e-05</td>\n",
       "      <td>2.401781e-05</td>\n",
       "      <td>2.012828e-05</td>\n",
       "      <td>1.742522e-05</td>\n",
       "      <td>1.520944e-05</td>\n",
       "      <td>1.237842e-05</td>\n",
       "      <td>9.985276e-06</td>\n",
       "      <td>7.871450e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>1933583</td>\n",
       "      <td>273</td>\n",
       "      <td>423.789150</td>\n",
       "      <td>55</td>\n",
       "      <td>904.812139</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.291110</td>\n",
       "      <td>0.250445</td>\n",
       "      <td>0.169895</td>\n",
       "      <td>0.156513</td>\n",
       "      <td>0.149891</td>\n",
       "      <td>0.140549</td>\n",
       "      <td>0.136163</td>\n",
       "      <td>0.133500</td>\n",
       "      <td>0.131674</td>\n",
       "      <td>0.130325</td>\n",
       "      <td>0.128449</td>\n",
       "      <td>0.127189</td>\n",
       "      <td>0.126279</td>\n",
       "      <td>0.125583</td>\n",
       "      <td>0.125034</td>\n",
       "      <td>0.124589</td>\n",
       "      <td>0.124219</td>\n",
       "      <td>0.123520</td>\n",
       "      <td>0.123026</td>\n",
       "      <td>0.122371</td>\n",
       "      <td>0.121950</td>\n",
       "      <td>0.121658</td>\n",
       "      <td>0.121360</td>\n",
       "      <td>0.121149</td>\n",
       "      <td>0.120999</td>\n",
       "      <td>0.120879</td>\n",
       "      <td>0.120717</td>\n",
       "      <td>0.120601</td>\n",
       "      <td>0.120460</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.441110</td>\n",
       "      <td>0.400445</td>\n",
       "      <td>0.319895</td>\n",
       "      <td>0.306513</td>\n",
       "      <td>0.299891</td>\n",
       "      <td>0.290549</td>\n",
       "      <td>0.286163</td>\n",
       "      <td>0.283500</td>\n",
       "      <td>0.281674</td>\n",
       "      <td>0.280325</td>\n",
       "      <td>0.278449</td>\n",
       "      <td>0.277189</td>\n",
       "      <td>0.276279</td>\n",
       "      <td>0.275583</td>\n",
       "      <td>0.275034</td>\n",
       "      <td>0.274589</td>\n",
       "      <td>0.274219</td>\n",
       "      <td>0.273520</td>\n",
       "      <td>0.273026</td>\n",
       "      <td>0.272371</td>\n",
       "      <td>0.271950</td>\n",
       "      <td>0.271658</td>\n",
       "      <td>0.271360</td>\n",
       "      <td>0.271149</td>\n",
       "      <td>0.270999</td>\n",
       "      <td>0.270879</td>\n",
       "      <td>0.270717</td>\n",
       "      <td>0.270601</td>\n",
       "      <td>0.270460</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.895555</td>\n",
       "      <td>0.875222</td>\n",
       "      <td>0.834947</td>\n",
       "      <td>0.828257</td>\n",
       "      <td>0.824946</td>\n",
       "      <td>0.820274</td>\n",
       "      <td>0.818082</td>\n",
       "      <td>0.816750</td>\n",
       "      <td>0.815837</td>\n",
       "      <td>0.815163</td>\n",
       "      <td>0.814224</td>\n",
       "      <td>0.813595</td>\n",
       "      <td>0.813140</td>\n",
       "      <td>0.812791</td>\n",
       "      <td>0.812517</td>\n",
       "      <td>0.812295</td>\n",
       "      <td>0.812109</td>\n",
       "      <td>0.811760</td>\n",
       "      <td>0.811513</td>\n",
       "      <td>0.811185</td>\n",
       "      <td>0.810975</td>\n",
       "      <td>0.810829</td>\n",
       "      <td>0.810680</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.810499</td>\n",
       "      <td>0.810440</td>\n",
       "      <td>0.810358</td>\n",
       "      <td>0.810301</td>\n",
       "      <td>0.810230</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.085555</td>\n",
       "      <td>0.065222</td>\n",
       "      <td>0.024947</td>\n",
       "      <td>0.018257</td>\n",
       "      <td>0.014946</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>0.008082</td>\n",
       "      <td>0.006750</td>\n",
       "      <td>0.005837</td>\n",
       "      <td>0.005163</td>\n",
       "      <td>0.004224</td>\n",
       "      <td>0.003595</td>\n",
       "      <td>0.003140</td>\n",
       "      <td>0.002791</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>0.002295</td>\n",
       "      <td>0.002109</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.000975</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>0.000230</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.153871</td>\n",
       "      <td>0.174526</td>\n",
       "      <td>0.215027</td>\n",
       "      <td>0.221729</td>\n",
       "      <td>0.225044</td>\n",
       "      <td>0.229720</td>\n",
       "      <td>0.231914</td>\n",
       "      <td>0.233247</td>\n",
       "      <td>0.234160</td>\n",
       "      <td>0.234835</td>\n",
       "      <td>0.235774</td>\n",
       "      <td>0.236404</td>\n",
       "      <td>0.236859</td>\n",
       "      <td>0.237208</td>\n",
       "      <td>0.237482</td>\n",
       "      <td>0.237705</td>\n",
       "      <td>0.237890</td>\n",
       "      <td>0.238239</td>\n",
       "      <td>0.238487</td>\n",
       "      <td>0.238814</td>\n",
       "      <td>0.239025</td>\n",
       "      <td>0.239171</td>\n",
       "      <td>0.239320</td>\n",
       "      <td>0.239425</td>\n",
       "      <td>0.239500</td>\n",
       "      <td>0.239560</td>\n",
       "      <td>0.239642</td>\n",
       "      <td>0.239699</td>\n",
       "      <td>0.239770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.445003e-03</td>\n",
       "      <td>0.024778</td>\n",
       "      <td>0.065053</td>\n",
       "      <td>0.071743</td>\n",
       "      <td>0.075054</td>\n",
       "      <td>0.079726</td>\n",
       "      <td>0.081918</td>\n",
       "      <td>0.083250</td>\n",
       "      <td>0.084163</td>\n",
       "      <td>0.084837</td>\n",
       "      <td>0.085776</td>\n",
       "      <td>0.086405</td>\n",
       "      <td>0.086860</td>\n",
       "      <td>0.087209</td>\n",
       "      <td>0.087483</td>\n",
       "      <td>0.087705</td>\n",
       "      <td>0.087891</td>\n",
       "      <td>0.088240</td>\n",
       "      <td>0.088487</td>\n",
       "      <td>0.088815</td>\n",
       "      <td>0.089025</td>\n",
       "      <td>0.089171</td>\n",
       "      <td>0.089320</td>\n",
       "      <td>0.089426</td>\n",
       "      <td>0.089501</td>\n",
       "      <td>0.089560</td>\n",
       "      <td>0.089642</td>\n",
       "      <td>0.089699</td>\n",
       "      <td>0.089770</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.740351e-04</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.743162e-07</td>\n",
       "      <td>8.786868e-07</td>\n",
       "      <td>8.003862e-07</td>\n",
       "      <td>6.564379e-07</td>\n",
       "      <td>5.574365e-07</td>\n",
       "      <td>4.297783e-07</td>\n",
       "      <td>3.496653e-07</td>\n",
       "      <td>2.952282e-07</td>\n",
       "      <td>2.402807e-07</td>\n",
       "      <td>2.019010e-07</td>\n",
       "      <td>1.749330e-07</td>\n",
       "      <td>1.534401e-07</td>\n",
       "      <td>1.246258e-07</td>\n",
       "      <td>1.042629e-07</td>\n",
       "      <td>7.955867e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>1933584</td>\n",
       "      <td>273</td>\n",
       "      <td>0.014179</td>\n",
       "      <td>123</td>\n",
       "      <td>0.061371</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299997</td>\n",
       "      <td>0.299936</td>\n",
       "      <td>0.299872</td>\n",
       "      <td>0.299808</td>\n",
       "      <td>0.299616</td>\n",
       "      <td>0.299426</td>\n",
       "      <td>0.299236</td>\n",
       "      <td>0.299047</td>\n",
       "      <td>0.298859</td>\n",
       "      <td>0.298485</td>\n",
       "      <td>0.298115</td>\n",
       "      <td>0.297748</td>\n",
       "      <td>0.297383</td>\n",
       "      <td>0.297022</td>\n",
       "      <td>0.296664</td>\n",
       "      <td>0.296309</td>\n",
       "      <td>0.295435</td>\n",
       "      <td>0.294578</td>\n",
       "      <td>0.292916</td>\n",
       "      <td>0.291317</td>\n",
       "      <td>0.289778</td>\n",
       "      <td>0.287573</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.283492</td>\n",
       "      <td>0.281598</td>\n",
       "      <td>0.278061</td>\n",
       "      <td>0.274819</td>\n",
       "      <td>0.269058</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.449997</td>\n",
       "      <td>0.449936</td>\n",
       "      <td>0.449872</td>\n",
       "      <td>0.449808</td>\n",
       "      <td>0.449616</td>\n",
       "      <td>0.449426</td>\n",
       "      <td>0.449236</td>\n",
       "      <td>0.449047</td>\n",
       "      <td>0.448859</td>\n",
       "      <td>0.448485</td>\n",
       "      <td>0.448115</td>\n",
       "      <td>0.447748</td>\n",
       "      <td>0.447383</td>\n",
       "      <td>0.447022</td>\n",
       "      <td>0.446664</td>\n",
       "      <td>0.446309</td>\n",
       "      <td>0.445435</td>\n",
       "      <td>0.444578</td>\n",
       "      <td>0.442916</td>\n",
       "      <td>0.441317</td>\n",
       "      <td>0.439778</td>\n",
       "      <td>0.437573</td>\n",
       "      <td>0.435481</td>\n",
       "      <td>0.433492</td>\n",
       "      <td>0.431598</td>\n",
       "      <td>0.428061</td>\n",
       "      <td>0.424819</td>\n",
       "      <td>0.419058</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.899998</td>\n",
       "      <td>0.899968</td>\n",
       "      <td>0.899936</td>\n",
       "      <td>0.899904</td>\n",
       "      <td>0.899808</td>\n",
       "      <td>0.899713</td>\n",
       "      <td>0.899618</td>\n",
       "      <td>0.899524</td>\n",
       "      <td>0.899429</td>\n",
       "      <td>0.899243</td>\n",
       "      <td>0.899057</td>\n",
       "      <td>0.898874</td>\n",
       "      <td>0.898692</td>\n",
       "      <td>0.898511</td>\n",
       "      <td>0.898332</td>\n",
       "      <td>0.898155</td>\n",
       "      <td>0.897717</td>\n",
       "      <td>0.897289</td>\n",
       "      <td>0.896458</td>\n",
       "      <td>0.895659</td>\n",
       "      <td>0.894889</td>\n",
       "      <td>0.893786</td>\n",
       "      <td>0.892741</td>\n",
       "      <td>0.891746</td>\n",
       "      <td>0.890799</td>\n",
       "      <td>0.889030</td>\n",
       "      <td>0.887409</td>\n",
       "      <td>0.884529</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.089998</td>\n",
       "      <td>0.089968</td>\n",
       "      <td>0.089936</td>\n",
       "      <td>0.089904</td>\n",
       "      <td>0.089808</td>\n",
       "      <td>0.089713</td>\n",
       "      <td>0.089618</td>\n",
       "      <td>0.089524</td>\n",
       "      <td>0.089429</td>\n",
       "      <td>0.089243</td>\n",
       "      <td>0.089057</td>\n",
       "      <td>0.088874</td>\n",
       "      <td>0.088692</td>\n",
       "      <td>0.088511</td>\n",
       "      <td>0.088332</td>\n",
       "      <td>0.088155</td>\n",
       "      <td>0.087717</td>\n",
       "      <td>0.087289</td>\n",
       "      <td>0.086458</td>\n",
       "      <td>0.085659</td>\n",
       "      <td>0.084889</td>\n",
       "      <td>0.083786</td>\n",
       "      <td>0.082741</td>\n",
       "      <td>0.081746</td>\n",
       "      <td>0.080799</td>\n",
       "      <td>0.079030</td>\n",
       "      <td>0.077409</td>\n",
       "      <td>0.074529</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150001</td>\n",
       "      <td>0.150001</td>\n",
       "      <td>0.150002</td>\n",
       "      <td>0.150003</td>\n",
       "      <td>0.150004</td>\n",
       "      <td>0.150006</td>\n",
       "      <td>0.150008</td>\n",
       "      <td>0.150015</td>\n",
       "      <td>0.150024</td>\n",
       "      <td>0.150053</td>\n",
       "      <td>0.150098</td>\n",
       "      <td>0.150158</td>\n",
       "      <td>0.150282</td>\n",
       "      <td>0.150443</td>\n",
       "      <td>0.150640</td>\n",
       "      <td>0.150867</td>\n",
       "      <td>0.151393</td>\n",
       "      <td>0.151986</td>\n",
       "      <td>0.153254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.606052e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>0.001845</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>0.003542</td>\n",
       "      <td>0.004341</td>\n",
       "      <td>0.005111</td>\n",
       "      <td>0.006214</td>\n",
       "      <td>0.007259</td>\n",
       "      <td>0.008254</td>\n",
       "      <td>0.009201</td>\n",
       "      <td>0.010970</td>\n",
       "      <td>0.012591</td>\n",
       "      <td>0.015471</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.606052e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000756</td>\n",
       "      <td>0.000941</td>\n",
       "      <td>0.001123</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>1.480361e-03</td>\n",
       "      <td>1.655983e-03</td>\n",
       "      <td>1.829416e-03</td>\n",
       "      <td>2.253245e-03</td>\n",
       "      <td>2.662812e-03</td>\n",
       "      <td>3.436606e-03</td>\n",
       "      <td>4.145748e-03</td>\n",
       "      <td>4.793975e-03</td>\n",
       "      <td>5.649368e-03</td>\n",
       "      <td>6.372503e-03</td>\n",
       "      <td>6.974524e-03</td>\n",
       "      <td>7.467574e-03</td>\n",
       "      <td>8.183647e-03</td>\n",
       "      <td>8.618686e-03</td>\n",
       "      <td>8.963359e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>1933585</td>\n",
       "      <td>273</td>\n",
       "      <td>0.045442</td>\n",
       "      <td>179</td>\n",
       "      <td>1.544173</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299990</td>\n",
       "      <td>0.299800</td>\n",
       "      <td>0.299600</td>\n",
       "      <td>0.299402</td>\n",
       "      <td>0.298811</td>\n",
       "      <td>0.298229</td>\n",
       "      <td>0.297655</td>\n",
       "      <td>0.297089</td>\n",
       "      <td>0.296530</td>\n",
       "      <td>0.295433</td>\n",
       "      <td>0.294364</td>\n",
       "      <td>0.293322</td>\n",
       "      <td>0.292306</td>\n",
       "      <td>0.291314</td>\n",
       "      <td>0.290345</td>\n",
       "      <td>0.289398</td>\n",
       "      <td>0.287123</td>\n",
       "      <td>0.284968</td>\n",
       "      <td>0.280977</td>\n",
       "      <td>0.277353</td>\n",
       "      <td>0.274038</td>\n",
       "      <td>0.269548</td>\n",
       "      <td>0.265541</td>\n",
       "      <td>0.261927</td>\n",
       "      <td>0.258642</td>\n",
       "      <td>0.252869</td>\n",
       "      <td>0.247927</td>\n",
       "      <td>0.239816</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.449999</td>\n",
       "      <td>0.449990</td>\n",
       "      <td>0.449800</td>\n",
       "      <td>0.449600</td>\n",
       "      <td>0.449402</td>\n",
       "      <td>0.448811</td>\n",
       "      <td>0.448229</td>\n",
       "      <td>0.447655</td>\n",
       "      <td>0.447089</td>\n",
       "      <td>0.446530</td>\n",
       "      <td>0.445433</td>\n",
       "      <td>0.444364</td>\n",
       "      <td>0.443322</td>\n",
       "      <td>0.442306</td>\n",
       "      <td>0.441314</td>\n",
       "      <td>0.440345</td>\n",
       "      <td>0.439398</td>\n",
       "      <td>0.437123</td>\n",
       "      <td>0.434968</td>\n",
       "      <td>0.430977</td>\n",
       "      <td>0.427353</td>\n",
       "      <td>0.424038</td>\n",
       "      <td>0.419548</td>\n",
       "      <td>0.415541</td>\n",
       "      <td>0.411927</td>\n",
       "      <td>0.408642</td>\n",
       "      <td>0.402869</td>\n",
       "      <td>0.397927</td>\n",
       "      <td>0.389816</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.899999</td>\n",
       "      <td>0.899995</td>\n",
       "      <td>0.899900</td>\n",
       "      <td>0.899800</td>\n",
       "      <td>0.899701</td>\n",
       "      <td>0.899406</td>\n",
       "      <td>0.899115</td>\n",
       "      <td>0.898828</td>\n",
       "      <td>0.898544</td>\n",
       "      <td>0.898265</td>\n",
       "      <td>0.897717</td>\n",
       "      <td>0.897182</td>\n",
       "      <td>0.896661</td>\n",
       "      <td>0.896153</td>\n",
       "      <td>0.895657</td>\n",
       "      <td>0.895172</td>\n",
       "      <td>0.894699</td>\n",
       "      <td>0.893561</td>\n",
       "      <td>0.892484</td>\n",
       "      <td>0.890489</td>\n",
       "      <td>0.888676</td>\n",
       "      <td>0.887019</td>\n",
       "      <td>0.884774</td>\n",
       "      <td>0.882771</td>\n",
       "      <td>0.880963</td>\n",
       "      <td>0.879321</td>\n",
       "      <td>0.876434</td>\n",
       "      <td>0.873964</td>\n",
       "      <td>0.869908</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.089999</td>\n",
       "      <td>0.089995</td>\n",
       "      <td>0.089900</td>\n",
       "      <td>0.089800</td>\n",
       "      <td>0.089701</td>\n",
       "      <td>0.089406</td>\n",
       "      <td>0.089115</td>\n",
       "      <td>0.088828</td>\n",
       "      <td>0.088544</td>\n",
       "      <td>0.088265</td>\n",
       "      <td>0.087717</td>\n",
       "      <td>0.087182</td>\n",
       "      <td>0.086661</td>\n",
       "      <td>0.086153</td>\n",
       "      <td>0.085657</td>\n",
       "      <td>0.085172</td>\n",
       "      <td>0.084699</td>\n",
       "      <td>0.083561</td>\n",
       "      <td>0.082484</td>\n",
       "      <td>0.080489</td>\n",
       "      <td>0.078676</td>\n",
       "      <td>0.077019</td>\n",
       "      <td>0.074774</td>\n",
       "      <td>0.072771</td>\n",
       "      <td>0.070963</td>\n",
       "      <td>0.069321</td>\n",
       "      <td>0.066434</td>\n",
       "      <td>0.063964</td>\n",
       "      <td>0.059908</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150002</td>\n",
       "      <td>0.150007</td>\n",
       "      <td>0.150015</td>\n",
       "      <td>0.150027</td>\n",
       "      <td>0.150045</td>\n",
       "      <td>0.150100</td>\n",
       "      <td>0.150179</td>\n",
       "      <td>0.150285</td>\n",
       "      <td>0.150412</td>\n",
       "      <td>0.150559</td>\n",
       "      <td>0.150723</td>\n",
       "      <td>0.150900</td>\n",
       "      <td>0.151379</td>\n",
       "      <td>0.151882</td>\n",
       "      <td>0.152888</td>\n",
       "      <td>0.153840</td>\n",
       "      <td>0.154721</td>\n",
       "      <td>0.155920</td>\n",
       "      <td>0.156989</td>\n",
       "      <td>0.157953</td>\n",
       "      <td>0.158828</td>\n",
       "      <td>0.160363</td>\n",
       "      <td>0.161674</td>\n",
       "      <td>0.163820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.021573e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>0.001735</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.002818</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>0.003847</td>\n",
       "      <td>0.004343</td>\n",
       "      <td>0.004828</td>\n",
       "      <td>0.005301</td>\n",
       "      <td>0.006439</td>\n",
       "      <td>0.007516</td>\n",
       "      <td>0.009511</td>\n",
       "      <td>0.011324</td>\n",
       "      <td>0.012981</td>\n",
       "      <td>0.015226</td>\n",
       "      <td>0.017229</td>\n",
       "      <td>0.019037</td>\n",
       "      <td>0.020679</td>\n",
       "      <td>0.023566</td>\n",
       "      <td>0.026036</td>\n",
       "      <td>0.030092</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.021573e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>0.000872</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>0.001401</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>0.002083</td>\n",
       "      <td>0.002459</td>\n",
       "      <td>0.002769</td>\n",
       "      <td>0.003023</td>\n",
       "      <td>3.224257e-03</td>\n",
       "      <td>3.380886e-03</td>\n",
       "      <td>3.500441e-03</td>\n",
       "      <td>3.680313e-03</td>\n",
       "      <td>3.751199e-03</td>\n",
       "      <td>3.734812e-03</td>\n",
       "      <td>3.642624e-03</td>\n",
       "      <td>3.538817e-03</td>\n",
       "      <td>3.387037e-03</td>\n",
       "      <td>3.250778e-03</td>\n",
       "      <td>3.130739e-03</td>\n",
       "      <td>3.023561e-03</td>\n",
       "      <td>2.840530e-03</td>\n",
       "      <td>2.689157e-03</td>\n",
       "      <td>2.451002e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>{'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...</td>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Temperature          A1  Ea1          A2  Ea2  A3  Ea3  \\\n",
       "1933581     1933581          273  261.472219  194    0.044413  141   0    0   \n",
       "1933582     1933582          273  596.074861   62    8.957436    9   0    0   \n",
       "1933583     1933583          273  423.789150   55  904.812139  184   0    0   \n",
       "1933584     1933584          273    0.014179  123    0.061371   74   0    0   \n",
       "1933585     1933585          273    0.045442  179    1.544173  121   0    0   \n",
       "\n",
       "         A4  Ea4  cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  \\\n",
       "1933581   0    0     0.3  0.294614  0.264231  0.182232  0.166794  0.158981   \n",
       "1933582   0    0     0.3  0.288006  0.241010  0.163040  0.150934  0.145032   \n",
       "1933583   0    0     0.3  0.291110  0.250445  0.169895  0.156513  0.149891   \n",
       "1933584   0    0     0.3  0.300000  0.299997  0.299936  0.299872  0.299808   \n",
       "1933585   0    0     0.3  0.299999  0.299990  0.299800  0.299600  0.299402   \n",
       "\n",
       "         cSM_120s  cSM_180s  cSM_240s  cSM_300s  cSM_360s  cSM_480s  cSM_600s  \\\n",
       "1933581  0.147680  0.142226  0.138849  0.136498  0.134742  0.132256  0.130561   \n",
       "1933582  0.136843  0.133071  0.130807  0.129271  0.128149  0.126603  0.125576   \n",
       "1933583  0.140549  0.136163  0.133500  0.131674  0.130325  0.128449  0.127189   \n",
       "1933584  0.299616  0.299426  0.299236  0.299047  0.298859  0.298485  0.298115   \n",
       "1933585  0.298811  0.298229  0.297655  0.297089  0.296530  0.295433  0.294364   \n",
       "\n",
       "         cSM_720s  cSM_840s  cSM_960s  cSM_1080s  cSM_1200s  cSM_1500s  \\\n",
       "1933581  0.129315  0.128356  0.127591   0.126961   0.126439   0.125435   \n",
       "1933582  0.124838  0.124282  0.123845   0.123492   0.123199   0.122650   \n",
       "1933583  0.126279  0.125583  0.125034   0.124589   0.124219   0.123520   \n",
       "1933584  0.297748  0.297383  0.297022   0.296664   0.296309   0.295435   \n",
       "1933585  0.293322  0.292306  0.291314   0.290345   0.289398   0.287123   \n",
       "\n",
       "         cSM_1800s  cSM_2400s  cSM_3000s  cSM_3600s  cSM_4500s  cSM_5400s  \\\n",
       "1933581   0.124712   0.123742   0.123113   0.122668   0.122203   0.121878   \n",
       "1933582   0.122268   0.121763   0.121446   0.121221   0.120997   0.120839   \n",
       "1933583   0.123026   0.122371   0.121950   0.121658   0.121360   0.121149   \n",
       "1933584   0.294578   0.292916   0.291317   0.289778   0.287573   0.285481   \n",
       "1933585   0.284968   0.280977   0.277353   0.274038   0.269548   0.265541   \n",
       "\n",
       "         cSM_6300s  cSM_7200s  cSM_9000s  cSM_10800s  cSM_14400s  cR_0s  \\\n",
       "1933581   0.121639   0.121454   0.121187    0.121005    0.120770   0.45   \n",
       "1933582   0.120729   0.120638   0.120521    0.120422    0.120333   0.45   \n",
       "1933583   0.120999   0.120879   0.120717    0.120601    0.120460   0.45   \n",
       "1933584   0.283492   0.281598   0.278061    0.274819    0.269058   0.45   \n",
       "1933585   0.261927   0.258642   0.252869    0.247927    0.239816   0.45   \n",
       "\n",
       "          cR_0.1s     cR_1s    cR_20s    cR_40s    cR_60s   cR_120s   cR_180s  \\\n",
       "1933581  0.444614  0.414231  0.332232  0.316794  0.308981  0.297680  0.292226   \n",
       "1933582  0.438006  0.391010  0.313040  0.300934  0.295032  0.286843  0.283071   \n",
       "1933583  0.441110  0.400445  0.319895  0.306513  0.299891  0.290549  0.286163   \n",
       "1933584  0.450000  0.449997  0.449936  0.449872  0.449808  0.449616  0.449426   \n",
       "1933585  0.449999  0.449990  0.449800  0.449600  0.449402  0.448811  0.448229   \n",
       "\n",
       "          cR_240s   cR_300s   cR_360s   cR_480s   cR_600s   cR_720s   cR_840s  \\\n",
       "1933581  0.288849  0.286498  0.284742  0.282256  0.280561  0.279315  0.278356   \n",
       "1933582  0.280807  0.279271  0.278149  0.276603  0.275576  0.274838  0.274282   \n",
       "1933583  0.283500  0.281674  0.280325  0.278449  0.277189  0.276279  0.275583   \n",
       "1933584  0.449236  0.449047  0.448859  0.448485  0.448115  0.447748  0.447383   \n",
       "1933585  0.447655  0.447089  0.446530  0.445433  0.444364  0.443322  0.442306   \n",
       "\n",
       "          cR_960s  cR_1080s  cR_1200s  cR_1500s  cR_1800s  cR_2400s  cR_3000s  \\\n",
       "1933581  0.277591  0.276961  0.276439  0.275435  0.274712  0.273742  0.273113   \n",
       "1933582  0.273845  0.273492  0.273199  0.272650  0.272268  0.271763  0.271446   \n",
       "1933583  0.275034  0.274589  0.274219  0.273520  0.273026  0.272371  0.271950   \n",
       "1933584  0.447022  0.446664  0.446309  0.445435  0.444578  0.442916  0.441317   \n",
       "1933585  0.441314  0.440345  0.439398  0.437123  0.434968  0.430977  0.427353   \n",
       "\n",
       "         cR_3600s  cR_4500s  cR_5400s  cR_6300s  cR_7200s  cR_9000s  \\\n",
       "1933581  0.272668  0.272203  0.271878  0.271639  0.271454  0.271187   \n",
       "1933582  0.271221  0.270997  0.270839  0.270729  0.270638  0.270521   \n",
       "1933583  0.271658  0.271360  0.271149  0.270999  0.270879  0.270717   \n",
       "1933584  0.439778  0.437573  0.435481  0.433492  0.431598  0.428061   \n",
       "1933585  0.424038  0.419548  0.415541  0.411927  0.408642  0.402869   \n",
       "\n",
       "         cR_10800s  cR_14400s  cB_0s   cB_0.1s     cB_1s    cB_20s    cB_40s  \\\n",
       "1933581   0.271005   0.270770    0.9  0.897307  0.882116  0.841116  0.833397   \n",
       "1933582   0.270422   0.270333    0.9  0.894003  0.870505  0.831520  0.825467   \n",
       "1933583   0.270601   0.270460    0.9  0.895555  0.875222  0.834947  0.828257   \n",
       "1933584   0.424819   0.419058    0.9  0.900000  0.899998  0.899968  0.899936   \n",
       "1933585   0.397927   0.389816    0.9  0.899999  0.899995  0.899900  0.899800   \n",
       "\n",
       "           cB_60s   cB_120s   cB_180s   cB_240s   cB_300s   cB_360s   cB_480s  \\\n",
       "1933581  0.829490  0.823840  0.821113  0.819425  0.818249  0.817371  0.816128   \n",
       "1933582  0.822516  0.818421  0.816535  0.815403  0.814636  0.814074  0.813302   \n",
       "1933583  0.824946  0.820274  0.818082  0.816750  0.815837  0.815163  0.814224   \n",
       "1933584  0.899904  0.899808  0.899713  0.899618  0.899524  0.899429  0.899243   \n",
       "1933585  0.899701  0.899406  0.899115  0.898828  0.898544  0.898265  0.897717   \n",
       "\n",
       "          cB_600s   cB_720s   cB_840s   cB_960s  cB_1080s  cB_1200s  cB_1500s  \\\n",
       "1933581  0.815280  0.814658  0.814178  0.813796  0.813481  0.813220  0.812717   \n",
       "1933582  0.812788  0.812419  0.812141  0.811922  0.811746  0.811599  0.811325   \n",
       "1933583  0.813595  0.813140  0.812791  0.812517  0.812295  0.812109  0.811760   \n",
       "1933584  0.899057  0.898874  0.898692  0.898511  0.898332  0.898155  0.897717   \n",
       "1933585  0.897182  0.896661  0.896153  0.895657  0.895172  0.894699  0.893561   \n",
       "\n",
       "         cB_1800s  cB_2400s  cB_3000s  cB_3600s  cB_4500s  cB_5400s  cB_6300s  \\\n",
       "1933581  0.812356  0.811871  0.811556  0.811334  0.811101  0.810939  0.810820   \n",
       "1933582  0.811134  0.810881  0.810723  0.810611  0.810498  0.810420  0.810364   \n",
       "1933583  0.811513  0.811185  0.810975  0.810829  0.810680  0.810574  0.810499   \n",
       "1933584  0.897289  0.896458  0.895659  0.894889  0.893786  0.892741  0.891746   \n",
       "1933585  0.892484  0.890489  0.888676  0.887019  0.884774  0.882771  0.880963   \n",
       "\n",
       "         cB_7200s  cB_9000s  cB_10800s  cB_14400s  cC_0s   cC_0.1s     cC_1s  \\\n",
       "1933581  0.810727  0.810594   0.810503   0.810385   0.09  0.087307  0.072116   \n",
       "1933582  0.810319  0.810260   0.810211   0.810166   0.09  0.084003  0.060505   \n",
       "1933583  0.810440  0.810358   0.810301   0.810230   0.09  0.085555  0.065222   \n",
       "1933584  0.890799  0.889030   0.887409   0.884529   0.09  0.090000  0.089998   \n",
       "1933585  0.879321  0.876434   0.873964   0.869908   0.09  0.089999  0.089995   \n",
       "\n",
       "           cC_20s    cC_40s    cC_60s   cC_120s   cC_180s   cC_240s   cC_300s  \\\n",
       "1933581  0.031116  0.023397  0.019490  0.013840  0.011113  0.009425  0.008249   \n",
       "1933582  0.021520  0.015467  0.012516  0.008421  0.006535  0.005403  0.004636   \n",
       "1933583  0.024947  0.018257  0.014946  0.010274  0.008082  0.006750  0.005837   \n",
       "1933584  0.089968  0.089936  0.089904  0.089808  0.089713  0.089618  0.089524   \n",
       "1933585  0.089900  0.089800  0.089701  0.089406  0.089115  0.088828  0.088544   \n",
       "\n",
       "          cC_360s   cC_480s   cC_600s   cC_720s   cC_840s   cC_960s  cC_1080s  \\\n",
       "1933581  0.007371  0.006128  0.005280  0.004658  0.004178  0.003796  0.003481   \n",
       "1933582  0.004074  0.003302  0.002788  0.002419  0.002141  0.001922  0.001746   \n",
       "1933583  0.005163  0.004224  0.003595  0.003140  0.002791  0.002517  0.002295   \n",
       "1933584  0.089429  0.089243  0.089057  0.088874  0.088692  0.088511  0.088332   \n",
       "1933585  0.088265  0.087717  0.087182  0.086661  0.086153  0.085657  0.085172   \n",
       "\n",
       "         cC_1200s  cC_1500s  cC_1800s  cC_2400s  cC_3000s  cC_3600s  cC_4500s  \\\n",
       "1933581  0.003220  0.002717  0.002356  0.001871  0.001556  0.001334  0.001101   \n",
       "1933582  0.001599  0.001325  0.001134  0.000881  0.000723  0.000611  0.000498   \n",
       "1933583  0.002109  0.001760  0.001513  0.001185  0.000975  0.000829  0.000680   \n",
       "1933584  0.088155  0.087717  0.087289  0.086458  0.085659  0.084889  0.083786   \n",
       "1933585  0.084699  0.083561  0.082484  0.080489  0.078676  0.077019  0.074774   \n",
       "\n",
       "         cC_5400s  cC_6300s  cC_7200s  cC_9000s  cC_10800s  cC_14400s  cP_0s  \\\n",
       "1933581  0.000939  0.000820  0.000727  0.000594   0.000503   0.000385   0.15   \n",
       "1933582  0.000420  0.000364  0.000319  0.000260   0.000211   0.000166   0.15   \n",
       "1933583  0.000574  0.000499  0.000440  0.000358   0.000301   0.000230   0.15   \n",
       "1933584  0.082741  0.081746  0.080799  0.079030   0.077409   0.074529   0.15   \n",
       "1933585  0.072771  0.070963  0.069321  0.066434   0.063964   0.059908   0.15   \n",
       "\n",
       "          cP_0.1s     cP_1s    cP_20s    cP_40s    cP_60s   cP_120s   cP_180s  \\\n",
       "1933581  0.150001  0.150033  0.151533  0.152891  0.154049  0.156821  0.158977   \n",
       "1933582  0.150234  0.158998  0.214963  0.222816  0.226315  0.230952  0.233026   \n",
       "1933583  0.153871  0.174526  0.215027  0.221729  0.225044  0.229720  0.231914   \n",
       "1933584  0.150000  0.150000  0.150000  0.150000  0.150000  0.150000  0.150000   \n",
       "1933585  0.150000  0.150000  0.150000  0.150000  0.150000  0.150002  0.150007   \n",
       "\n",
       "          cP_240s   cP_300s   cP_360s   cP_480s   cP_600s   cP_720s   cP_840s  \\\n",
       "1933581  0.160764  0.162300  0.163651  0.165951  0.167870  0.169517  0.170960   \n",
       "1933582  0.234256  0.235084  0.235687  0.236513  0.237059  0.237451  0.237746   \n",
       "1933583  0.233247  0.234160  0.234835  0.235774  0.236404  0.236859  0.237208   \n",
       "1933584  0.150000  0.150000  0.150000  0.150001  0.150001  0.150002  0.150003   \n",
       "1933585  0.150015  0.150027  0.150045  0.150100  0.150179  0.150285  0.150412   \n",
       "\n",
       "          cP_960s  cP_1080s  cP_1200s  cP_1500s  cP_1800s  cP_2400s  cP_3000s  \\\n",
       "1933581  0.172244  0.173401  0.174452  0.176724  0.178618  0.181647  0.184013   \n",
       "1933582  0.237978  0.238164  0.238319  0.238608  0.238809  0.239075  0.239242   \n",
       "1933583  0.237482  0.237705  0.237890  0.238239  0.238487  0.238814  0.239025   \n",
       "1933584  0.150004  0.150006  0.150008  0.150015  0.150024  0.150053  0.150098   \n",
       "1933585  0.150559  0.150723  0.150900  0.151379  0.151882  0.152888  0.153840   \n",
       "\n",
       "         cP_3600s  cP_4500s  cP_5400s  cP_6300s  cP_7200s  cP_9000s  \\\n",
       "1933581  0.185946  0.188297  0.190198  0.191782  0.193140  0.195365   \n",
       "1933582  0.239360  0.239477  0.239560  0.239618  0.239666  0.239727   \n",
       "1933583  0.239171  0.239320  0.239425  0.239500  0.239560  0.239642   \n",
       "1933584  0.150158  0.150282  0.150443  0.150640  0.150867  0.151393   \n",
       "1933585  0.154721  0.155920  0.156989  0.157953  0.158828  0.160363   \n",
       "\n",
       "         cP_10800s  cP_14400s  cIMP1_0s    cIMP1_0.1s  cIMP1_1s  cIMP1_20s  \\\n",
       "1933581   0.197137   0.199840       0.0  2.693084e-03  0.017884   0.058884   \n",
       "1933582   0.239779   0.239826       0.0  5.997070e-03  0.029495   0.068480   \n",
       "1933583   0.239699   0.239770       0.0  4.445003e-03  0.024778   0.065053   \n",
       "1933584   0.151986   0.153254       0.0  1.606052e-07  0.000002   0.000032   \n",
       "1933585   0.161674   0.163820       0.0  5.021573e-07  0.000005   0.000100   \n",
       "\n",
       "         cIMP1_40s  cIMP1_60s  cIMP1_120s  cIMP1_180s  cIMP1_240s  cIMP1_300s  \\\n",
       "1933581   0.066603   0.070510    0.076160    0.078887    0.080575    0.081751   \n",
       "1933582   0.074533   0.077484    0.081579    0.083465    0.084597    0.085364   \n",
       "1933583   0.071743   0.075054    0.079726    0.081918    0.083250    0.084163   \n",
       "1933584   0.000064   0.000096    0.000192    0.000287    0.000382    0.000476   \n",
       "1933585   0.000200   0.000299    0.000594    0.000885    0.001172    0.001456   \n",
       "\n",
       "         cIMP1_360s  cIMP1_480s  cIMP1_600s  cIMP1_720s  cIMP1_840s  \\\n",
       "1933581    0.082629    0.083872    0.084720    0.085342    0.085822   \n",
       "1933582    0.085926    0.086698    0.087212    0.087581    0.087859   \n",
       "1933583    0.084837    0.085776    0.086405    0.086860    0.087209   \n",
       "1933584    0.000571    0.000757    0.000943    0.001126    0.001308   \n",
       "1933585    0.001735    0.002283    0.002818    0.003339    0.003847   \n",
       "\n",
       "         cIMP1_960s  cIMP1_1080s  cIMP1_1200s  cIMP1_1500s  cIMP1_1800s  \\\n",
       "1933581    0.086204     0.086519     0.086780     0.087283     0.087644   \n",
       "1933582    0.088078     0.088254     0.088401     0.088675     0.088866   \n",
       "1933583    0.087483     0.087705     0.087891     0.088240     0.088487   \n",
       "1933584    0.001489     0.001668     0.001845     0.002283     0.002711   \n",
       "1933585    0.004343     0.004828     0.005301     0.006439     0.007516   \n",
       "\n",
       "         cIMP1_2400s  cIMP1_3000s  cIMP1_3600s  cIMP1_4500s  cIMP1_5400s  \\\n",
       "1933581     0.088129     0.088444     0.088666     0.088899     0.089061   \n",
       "1933582     0.089119     0.089277     0.089389     0.089502     0.089580   \n",
       "1933583     0.088815     0.089025     0.089171     0.089320     0.089426   \n",
       "1933584     0.003542     0.004341     0.005111     0.006214     0.007259   \n",
       "1933585     0.009511     0.011324     0.012981     0.015226     0.017229   \n",
       "\n",
       "         cIMP1_6300s  cIMP1_7200s  cIMP1_9000s  cIMP1_10800s  cIMP1_14400s  \\\n",
       "1933581     0.089180     0.089273     0.089406      0.089497      0.089615   \n",
       "1933582     0.089636     0.089681     0.089740      0.089789      0.089834   \n",
       "1933583     0.089501     0.089560     0.089642      0.089699      0.089770   \n",
       "1933584     0.008254     0.009201     0.010970      0.012591      0.015471   \n",
       "1933585     0.019037     0.020679     0.023566      0.026036      0.030092   \n",
       "\n",
       "         cIMP2_0s  cIMP2_0.1s  cIMP2_1s  cIMP2_20s  cIMP2_40s  cIMP2_60s  \\\n",
       "1933581         0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933582         0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933583         0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933584         0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933585         0         0.0       0.0        0.0        0.0        0.0   \n",
       "\n",
       "         cIMP2_120s  cIMP2_180s  cIMP2_240s  cIMP2_300s  cIMP2_360s  \\\n",
       "1933581         0.0         0.0         0.0         0.0         0.0   \n",
       "1933582         0.0         0.0         0.0         0.0         0.0   \n",
       "1933583         0.0         0.0         0.0         0.0         0.0   \n",
       "1933584         0.0         0.0         0.0         0.0         0.0   \n",
       "1933585         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "         cIMP2_480s  cIMP2_600s  cIMP2_720s  cIMP2_840s  cIMP2_960s  \\\n",
       "1933581         0.0         0.0         0.0         0.0         0.0   \n",
       "1933582         0.0         0.0         0.0         0.0         0.0   \n",
       "1933583         0.0         0.0         0.0         0.0         0.0   \n",
       "1933584         0.0         0.0         0.0         0.0         0.0   \n",
       "1933585         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "         cIMP2_1080s  cIMP2_1200s  cIMP2_1500s  cIMP2_1800s  cIMP2_2400s  \\\n",
       "1933581          0.0          0.0          0.0          0.0          0.0   \n",
       "1933582          0.0          0.0          0.0          0.0          0.0   \n",
       "1933583          0.0          0.0          0.0          0.0          0.0   \n",
       "1933584          0.0          0.0          0.0          0.0          0.0   \n",
       "1933585          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cIMP2_3000s  cIMP2_3600s  cIMP2_4500s  cIMP2_5400s  cIMP2_6300s  \\\n",
       "1933581          0.0          0.0          0.0          0.0          0.0   \n",
       "1933582          0.0          0.0          0.0          0.0          0.0   \n",
       "1933583          0.0          0.0          0.0          0.0          0.0   \n",
       "1933584          0.0          0.0          0.0          0.0          0.0   \n",
       "1933585          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cIMP2_7200s  cIMP2_9000s  cIMP2_10800s  cIMP2_14400s  cINT1_0s  \\\n",
       "1933581          0.0          0.0           0.0           0.0         0   \n",
       "1933582          0.0          0.0           0.0           0.0         0   \n",
       "1933583          0.0          0.0           0.0           0.0         0   \n",
       "1933584          0.0          0.0           0.0           0.0         0   \n",
       "1933585          0.0          0.0           0.0           0.0         0   \n",
       "\n",
       "           cINT1_0.1s  cINT1_1s  cINT1_20s  cINT1_40s  cINT1_60s  cINT1_120s  \\\n",
       "1933581  2.692579e-03  0.017851   0.057351   0.063712   0.066461    0.069339   \n",
       "1933582  5.762906e-03  0.020497   0.003516   0.001717   0.001169    0.000626   \n",
       "1933583  5.740351e-04  0.000252   0.000026   0.000014   0.000010    0.000006   \n",
       "1933584  1.606052e-07  0.000002   0.000032   0.000064   0.000096    0.000192   \n",
       "1933585  5.021573e-07  0.000005   0.000100   0.000200   0.000299    0.000590   \n",
       "\n",
       "         cINT1_180s  cINT1_240s  cINT1_300s  cINT1_360s  cINT1_480s  \\\n",
       "1933581    0.069910    0.069811    0.069451    0.068978    0.067921   \n",
       "1933582    0.000438    0.000341    0.000280    0.000239    0.000186   \n",
       "1933583    0.000004    0.000003    0.000003    0.000002    0.000002   \n",
       "1933584    0.000287    0.000382    0.000476    0.000570    0.000756   \n",
       "1933585    0.000872    0.001143    0.001401    0.001645    0.002083   \n",
       "\n",
       "         cINT1_600s  cINT1_720s  cINT1_840s    cINT1_960s   cINT1_1080s  \\\n",
       "1933581    0.066850    0.065826    0.064862  6.396068e-02  6.311790e-02   \n",
       "1933582    0.000152    0.000130    0.000113  1.001867e-04  9.011584e-05   \n",
       "1933583    0.000001    0.000001    0.000001  9.743162e-07  8.786868e-07   \n",
       "1933584    0.000941    0.001123    0.001303  1.480361e-03  1.655983e-03   \n",
       "1933585    0.002459    0.002769    0.003023  3.224257e-03  3.380886e-03   \n",
       "\n",
       "          cINT1_1200s   cINT1_1500s   cINT1_1800s   cINT1_2400s   cINT1_3000s  \\\n",
       "1933581  6.232886e-02  6.055913e-02  5.902615e-02  5.648263e-02  5.443118e-02   \n",
       "1933582  8.187918e-05  6.681850e-05  5.660047e-05  4.337807e-05  3.526312e-05   \n",
       "1933583  8.003862e-07  6.564379e-07  5.574365e-07  4.297783e-07  3.496653e-07   \n",
       "1933584  1.829416e-03  2.253245e-03  2.662812e-03  3.436606e-03  4.145748e-03   \n",
       "1933585  3.500441e-03  3.680313e-03  3.751199e-03  3.734812e-03  3.642624e-03   \n",
       "\n",
       "          cINT1_3600s   cINT1_4500s   cINT1_5400s   cINT1_6300s   cINT1_7200s  \\\n",
       "1933581  5.271993e-02  5.060185e-02  4.886322e-02  4.739870e-02  4.613309e-02   \n",
       "1933582  2.960174e-05  2.401781e-05  2.012828e-05  1.742522e-05  1.520944e-05   \n",
       "1933583  2.952282e-07  2.402807e-07  2.019010e-07  1.749330e-07  1.534401e-07   \n",
       "1933584  4.793975e-03  5.649368e-03  6.372503e-03  6.974524e-03  7.467574e-03   \n",
       "1933585  3.538817e-03  3.387037e-03  3.250778e-03  3.130739e-03  3.023561e-03   \n",
       "\n",
       "          cINT1_9000s  cINT1_10800s  cINT1_14400s  Fast_rxn1  Medium_rxn1  \\\n",
       "1933581  4.404100e-02  4.236088e-02  3.977472e-02          1            0   \n",
       "1933582  1.237842e-05  9.985276e-06  7.871450e-06          1            0   \n",
       "1933583  1.246258e-07  1.042629e-07  7.955867e-08          1            0   \n",
       "1933584  8.183647e-03  8.618686e-03  8.963359e-03          0            0   \n",
       "1933585  2.840530e-03  2.689157e-03  2.451002e-03          0            0   \n",
       "\n",
       "         Slow_rxn1  Fast_rxn2  Medium_rxn2  Slow_rxn2  \\\n",
       "1933581          0          0            0          1   \n",
       "1933582          0          0            1          0   \n",
       "1933583          0          1            0          0   \n",
       "1933584          1          0            0          1   \n",
       "1933585          1          0            1          0   \n",
       "\n",
       "                                            Reaction_order  Mechanism  \n",
       "1933581  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933582  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933583  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933584  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  \n",
       "1933585  {'rxn1': {'SM': 2, 'C': 2, 'B': 2, 'R': 2}, 'r...  (93, 254)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.read_csv('../two_reactions_022624.csv')\n",
    "df_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "na_counts = df_raw.isna().sum().sum()\n",
    "print(na_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
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       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_raw_df = df_raw.iloc[:, -1:]\n",
    "y_raw_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [Mechanism]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "rows_with_nan = y_raw_df[y_raw_df.isna().any(axis=1)]\n",
    "print(rows_with_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_values = y_raw_df['Mechanism'].unique() # It 1 more than  y because of NaN \n",
    "print(len(unique_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 258)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_raw.copy()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_list = [col for col in df.columns if col.endswith('s')]\n",
    "# conc_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>cSM_360s</th>\n",
       "      <th>cSM_480s</th>\n",
       "      <th>cSM_600s</th>\n",
       "      <th>cSM_720s</th>\n",
       "      <th>cSM_840s</th>\n",
       "      <th>cSM_960s</th>\n",
       "      <th>cSM_1080s</th>\n",
       "      <th>cSM_1200s</th>\n",
       "      <th>cSM_1500s</th>\n",
       "      <th>cSM_1800s</th>\n",
       "      <th>cSM_2400s</th>\n",
       "      <th>cSM_3000s</th>\n",
       "      <th>cSM_3600s</th>\n",
       "      <th>cSM_4500s</th>\n",
       "      <th>cSM_5400s</th>\n",
       "      <th>cSM_6300s</th>\n",
       "      <th>cSM_7200s</th>\n",
       "      <th>cSM_9000s</th>\n",
       "      <th>cSM_10800s</th>\n",
       "      <th>cSM_14400s</th>\n",
       "      <th>cR_0s</th>\n",
       "      <th>cR_0.1s</th>\n",
       "      <th>cR_1s</th>\n",
       "      <th>cR_20s</th>\n",
       "      <th>cR_40s</th>\n",
       "      <th>cR_60s</th>\n",
       "      <th>cR_120s</th>\n",
       "      <th>cR_180s</th>\n",
       "      <th>cR_240s</th>\n",
       "      <th>cR_300s</th>\n",
       "      <th>cR_360s</th>\n",
       "      <th>cR_480s</th>\n",
       "      <th>cR_600s</th>\n",
       "      <th>cR_720s</th>\n",
       "      <th>cR_840s</th>\n",
       "      <th>cR_960s</th>\n",
       "      <th>cR_1080s</th>\n",
       "      <th>cR_1200s</th>\n",
       "      <th>cR_1500s</th>\n",
       "      <th>cR_1800s</th>\n",
       "      <th>cR_2400s</th>\n",
       "      <th>cR_3000s</th>\n",
       "      <th>cR_3600s</th>\n",
       "      <th>cR_4500s</th>\n",
       "      <th>cR_5400s</th>\n",
       "      <th>cR_6300s</th>\n",
       "      <th>cR_7200s</th>\n",
       "      <th>cR_9000s</th>\n",
       "      <th>cR_10800s</th>\n",
       "      <th>cR_14400s</th>\n",
       "      <th>cB_0s</th>\n",
       "      <th>cB_0.1s</th>\n",
       "      <th>cB_1s</th>\n",
       "      <th>cB_20s</th>\n",
       "      <th>cB_40s</th>\n",
       "      <th>cB_60s</th>\n",
       "      <th>cB_120s</th>\n",
       "      <th>cB_180s</th>\n",
       "      <th>cB_240s</th>\n",
       "      <th>cB_300s</th>\n",
       "      <th>cB_360s</th>\n",
       "      <th>cB_480s</th>\n",
       "      <th>cB_600s</th>\n",
       "      <th>cB_720s</th>\n",
       "      <th>cB_840s</th>\n",
       "      <th>cB_960s</th>\n",
       "      <th>cB_1080s</th>\n",
       "      <th>cB_1200s</th>\n",
       "      <th>cB_1500s</th>\n",
       "      <th>cB_1800s</th>\n",
       "      <th>cB_2400s</th>\n",
       "      <th>cB_3000s</th>\n",
       "      <th>cB_3600s</th>\n",
       "      <th>cB_4500s</th>\n",
       "      <th>cB_5400s</th>\n",
       "      <th>cB_6300s</th>\n",
       "      <th>cB_7200s</th>\n",
       "      <th>cB_9000s</th>\n",
       "      <th>cB_10800s</th>\n",
       "      <th>cB_14400s</th>\n",
       "      <th>cC_0s</th>\n",
       "      <th>cC_0.1s</th>\n",
       "      <th>cC_1s</th>\n",
       "      <th>cC_20s</th>\n",
       "      <th>cC_40s</th>\n",
       "      <th>cC_60s</th>\n",
       "      <th>cC_120s</th>\n",
       "      <th>cC_180s</th>\n",
       "      <th>cC_240s</th>\n",
       "      <th>cC_300s</th>\n",
       "      <th>cC_360s</th>\n",
       "      <th>cC_480s</th>\n",
       "      <th>cC_600s</th>\n",
       "      <th>cC_720s</th>\n",
       "      <th>cC_840s</th>\n",
       "      <th>cC_960s</th>\n",
       "      <th>cC_1080s</th>\n",
       "      <th>cC_1200s</th>\n",
       "      <th>cC_1500s</th>\n",
       "      <th>cC_1800s</th>\n",
       "      <th>cC_2400s</th>\n",
       "      <th>cC_3000s</th>\n",
       "      <th>cC_3600s</th>\n",
       "      <th>cC_4500s</th>\n",
       "      <th>cC_5400s</th>\n",
       "      <th>cC_6300s</th>\n",
       "      <th>cC_7200s</th>\n",
       "      <th>cC_9000s</th>\n",
       "      <th>cC_10800s</th>\n",
       "      <th>cC_14400s</th>\n",
       "      <th>cP_0s</th>\n",
       "      <th>cP_0.1s</th>\n",
       "      <th>cP_1s</th>\n",
       "      <th>cP_20s</th>\n",
       "      <th>cP_40s</th>\n",
       "      <th>cP_60s</th>\n",
       "      <th>cP_120s</th>\n",
       "      <th>cP_180s</th>\n",
       "      <th>cP_240s</th>\n",
       "      <th>cP_300s</th>\n",
       "      <th>cP_360s</th>\n",
       "      <th>cP_480s</th>\n",
       "      <th>cP_600s</th>\n",
       "      <th>cP_720s</th>\n",
       "      <th>cP_840s</th>\n",
       "      <th>cP_960s</th>\n",
       "      <th>cP_1080s</th>\n",
       "      <th>cP_1200s</th>\n",
       "      <th>cP_1500s</th>\n",
       "      <th>cP_1800s</th>\n",
       "      <th>cP_2400s</th>\n",
       "      <th>cP_3000s</th>\n",
       "      <th>cP_3600s</th>\n",
       "      <th>cP_4500s</th>\n",
       "      <th>cP_5400s</th>\n",
       "      <th>cP_6300s</th>\n",
       "      <th>cP_7200s</th>\n",
       "      <th>cP_9000s</th>\n",
       "      <th>cP_10800s</th>\n",
       "      <th>cP_14400s</th>\n",
       "      <th>cIMP1_0s</th>\n",
       "      <th>cIMP1_0.1s</th>\n",
       "      <th>cIMP1_1s</th>\n",
       "      <th>cIMP1_20s</th>\n",
       "      <th>cIMP1_40s</th>\n",
       "      <th>cIMP1_60s</th>\n",
       "      <th>cIMP1_120s</th>\n",
       "      <th>cIMP1_180s</th>\n",
       "      <th>cIMP1_240s</th>\n",
       "      <th>cIMP1_300s</th>\n",
       "      <th>cIMP1_360s</th>\n",
       "      <th>cIMP1_480s</th>\n",
       "      <th>cIMP1_600s</th>\n",
       "      <th>cIMP1_720s</th>\n",
       "      <th>cIMP1_840s</th>\n",
       "      <th>cIMP1_960s</th>\n",
       "      <th>cIMP1_1080s</th>\n",
       "      <th>cIMP1_1200s</th>\n",
       "      <th>cIMP1_1500s</th>\n",
       "      <th>cIMP1_1800s</th>\n",
       "      <th>cIMP1_2400s</th>\n",
       "      <th>cIMP1_3000s</th>\n",
       "      <th>cIMP1_3600s</th>\n",
       "      <th>cIMP1_4500s</th>\n",
       "      <th>cIMP1_5400s</th>\n",
       "      <th>cIMP1_6300s</th>\n",
       "      <th>cIMP1_7200s</th>\n",
       "      <th>cIMP1_9000s</th>\n",
       "      <th>cIMP1_10800s</th>\n",
       "      <th>cIMP1_14400s</th>\n",
       "      <th>cIMP2_0s</th>\n",
       "      <th>cIMP2_0.1s</th>\n",
       "      <th>cIMP2_1s</th>\n",
       "      <th>cIMP2_20s</th>\n",
       "      <th>cIMP2_40s</th>\n",
       "      <th>cIMP2_60s</th>\n",
       "      <th>cIMP2_120s</th>\n",
       "      <th>cIMP2_180s</th>\n",
       "      <th>cIMP2_240s</th>\n",
       "      <th>cIMP2_300s</th>\n",
       "      <th>cIMP2_360s</th>\n",
       "      <th>cIMP2_480s</th>\n",
       "      <th>cIMP2_600s</th>\n",
       "      <th>cIMP2_720s</th>\n",
       "      <th>cIMP2_840s</th>\n",
       "      <th>cIMP2_960s</th>\n",
       "      <th>cIMP2_1080s</th>\n",
       "      <th>cIMP2_1200s</th>\n",
       "      <th>cIMP2_1500s</th>\n",
       "      <th>cIMP2_1800s</th>\n",
       "      <th>cIMP2_2400s</th>\n",
       "      <th>cIMP2_3000s</th>\n",
       "      <th>cIMP2_3600s</th>\n",
       "      <th>cIMP2_4500s</th>\n",
       "      <th>cIMP2_5400s</th>\n",
       "      <th>cIMP2_6300s</th>\n",
       "      <th>cIMP2_7200s</th>\n",
       "      <th>cIMP2_9000s</th>\n",
       "      <th>cIMP2_10800s</th>\n",
       "      <th>cIMP2_14400s</th>\n",
       "      <th>cINT1_0s</th>\n",
       "      <th>cINT1_0.1s</th>\n",
       "      <th>cINT1_1s</th>\n",
       "      <th>cINT1_20s</th>\n",
       "      <th>cINT1_40s</th>\n",
       "      <th>cINT1_60s</th>\n",
       "      <th>cINT1_120s</th>\n",
       "      <th>cINT1_180s</th>\n",
       "      <th>cINT1_240s</th>\n",
       "      <th>cINT1_300s</th>\n",
       "      <th>cINT1_360s</th>\n",
       "      <th>cINT1_480s</th>\n",
       "      <th>cINT1_600s</th>\n",
       "      <th>cINT1_720s</th>\n",
       "      <th>cINT1_840s</th>\n",
       "      <th>cINT1_960s</th>\n",
       "      <th>cINT1_1080s</th>\n",
       "      <th>cINT1_1200s</th>\n",
       "      <th>cINT1_1500s</th>\n",
       "      <th>cINT1_1800s</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299992</td>\n",
       "      <td>0.299837</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>0.299514</td>\n",
       "      <td>0.299035</td>\n",
       "      <td>0.298564</td>\n",
       "      <td>0.298101</td>\n",
       "      <td>0.297645</td>\n",
       "      <td>0.297197</td>\n",
       "      <td>0.296321</td>\n",
       "      <td>0.295471</td>\n",
       "      <td>0.294644</td>\n",
       "      <td>0.293842</td>\n",
       "      <td>0.293062</td>\n",
       "      <td>0.292304</td>\n",
       "      <td>0.291567</td>\n",
       "      <td>0.289810</td>\n",
       "      <td>0.288156</td>\n",
       "      <td>0.285124</td>\n",
       "      <td>0.282405</td>\n",
       "      <td>0.279936</td>\n",
       "      <td>0.276626</td>\n",
       "      <td>0.273681</td>\n",
       "      <td>0.271057</td>\n",
       "      <td>0.268655</td>\n",
       "      <td>0.264483</td>\n",
       "      <td>0.260906</td>\n",
       "      <td>0.255077</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.449999</td>\n",
       "      <td>0.449992</td>\n",
       "      <td>0.449837</td>\n",
       "      <td>0.449675</td>\n",
       "      <td>0.449513</td>\n",
       "      <td>0.449035</td>\n",
       "      <td>0.448563</td>\n",
       "      <td>0.448099</td>\n",
       "      <td>0.447641</td>\n",
       "      <td>0.447189</td>\n",
       "      <td>0.446304</td>\n",
       "      <td>0.445441</td>\n",
       "      <td>0.444591</td>\n",
       "      <td>0.443759</td>\n",
       "      <td>0.442944</td>\n",
       "      <td>0.442143</td>\n",
       "      <td>0.441357</td>\n",
       "      <td>0.439447</td>\n",
       "      <td>0.437585</td>\n",
       "      <td>0.434001</td>\n",
       "      <td>0.430589</td>\n",
       "      <td>0.427321</td>\n",
       "      <td>0.422705</td>\n",
       "      <td>0.418420</td>\n",
       "      <td>0.414446</td>\n",
       "      <td>0.410773</td>\n",
       "      <td>0.404216</td>\n",
       "      <td>0.398530</td>\n",
       "      <td>0.389175</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.899996</td>\n",
       "      <td>0.899918</td>\n",
       "      <td>0.899837</td>\n",
       "      <td>0.899757</td>\n",
       "      <td>0.899517</td>\n",
       "      <td>0.899282</td>\n",
       "      <td>0.899049</td>\n",
       "      <td>0.898820</td>\n",
       "      <td>0.898595</td>\n",
       "      <td>0.898152</td>\n",
       "      <td>0.897720</td>\n",
       "      <td>0.897296</td>\n",
       "      <td>0.896880</td>\n",
       "      <td>0.896472</td>\n",
       "      <td>0.896072</td>\n",
       "      <td>0.895679</td>\n",
       "      <td>0.894724</td>\n",
       "      <td>0.893792</td>\n",
       "      <td>0.892001</td>\n",
       "      <td>0.890294</td>\n",
       "      <td>0.888661</td>\n",
       "      <td>0.886353</td>\n",
       "      <td>0.884210</td>\n",
       "      <td>0.882223</td>\n",
       "      <td>0.880387</td>\n",
       "      <td>0.877108</td>\n",
       "      <td>0.874265</td>\n",
       "      <td>0.869588</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.089999</td>\n",
       "      <td>0.089992</td>\n",
       "      <td>0.089837</td>\n",
       "      <td>0.089675</td>\n",
       "      <td>0.089514</td>\n",
       "      <td>0.089035</td>\n",
       "      <td>0.088565</td>\n",
       "      <td>0.088102</td>\n",
       "      <td>0.087648</td>\n",
       "      <td>0.087201</td>\n",
       "      <td>0.086329</td>\n",
       "      <td>0.085486</td>\n",
       "      <td>0.084671</td>\n",
       "      <td>0.083883</td>\n",
       "      <td>0.083121</td>\n",
       "      <td>0.082384</td>\n",
       "      <td>0.081672</td>\n",
       "      <td>0.079992</td>\n",
       "      <td>0.078441</td>\n",
       "      <td>0.075686</td>\n",
       "      <td>0.073313</td>\n",
       "      <td>0.071243</td>\n",
       "      <td>0.068586</td>\n",
       "      <td>0.066312</td>\n",
       "      <td>0.064363</td>\n",
       "      <td>0.062596</td>\n",
       "      <td>0.059617</td>\n",
       "      <td>0.057093</td>\n",
       "      <td>0.053027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.088057e-07</td>\n",
       "      <td>4.087539e-06</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.002264</td>\n",
       "      <td>0.002678</td>\n",
       "      <td>0.003079</td>\n",
       "      <td>0.003469</td>\n",
       "      <td>0.003848</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.005095</td>\n",
       "      <td>0.005922</td>\n",
       "      <td>0.007438</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>0.011687</td>\n",
       "      <td>0.013159</td>\n",
       "      <td>0.014471</td>\n",
       "      <td>0.015673</td>\n",
       "      <td>0.017758</td>\n",
       "      <td>0.019547</td>\n",
       "      <td>0.022462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.070481e-17</td>\n",
       "      <td>8.090876e-14</td>\n",
       "      <td>6.970219e-10</td>\n",
       "      <td>5.645935e-09</td>\n",
       "      <td>1.811532e-08</td>\n",
       "      <td>1.627378e-07</td>\n",
       "      <td>5.814287e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000562</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.001960</td>\n",
       "      <td>0.002631</td>\n",
       "      <td>0.003305</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>0.006188</td>\n",
       "      <td>0.007951</td>\n",
       "      <td>0</td>\n",
       "      <td>4.088057e-07</td>\n",
       "      <td>4.087539e-06</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000717</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0.001394</td>\n",
       "      <td>0.001823</td>\n",
       "      <td>0.002234</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.003351</td>\n",
       "      <td>0.003688</td>\n",
       "      <td>0.004007</td>\n",
       "      <td>0.004732</td>\n",
       "      <td>0.005351</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.007791</td>\n",
       "      <td>0.007491</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.006560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299999</td>\n",
       "      <td>0.299978</td>\n",
       "      <td>0.299957</td>\n",
       "      <td>0.299935</td>\n",
       "      <td>0.299870</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.299740</td>\n",
       "      <td>0.299675</td>\n",
       "      <td>0.299611</td>\n",
       "      <td>0.299482</td>\n",
       "      <td>0.299355</td>\n",
       "      <td>0.299227</td>\n",
       "      <td>0.299100</td>\n",
       "      <td>0.298974</td>\n",
       "      <td>0.298848</td>\n",
       "      <td>0.298723</td>\n",
       "      <td>0.298412</td>\n",
       "      <td>0.298104</td>\n",
       "      <td>0.297497</td>\n",
       "      <td>0.296902</td>\n",
       "      <td>0.296318</td>\n",
       "      <td>0.295462</td>\n",
       "      <td>0.294630</td>\n",
       "      <td>0.293819</td>\n",
       "      <td>0.293030</td>\n",
       "      <td>0.291510</td>\n",
       "      <td>0.290056</td>\n",
       "      <td>0.287368</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.449999</td>\n",
       "      <td>0.449978</td>\n",
       "      <td>0.449957</td>\n",
       "      <td>0.449935</td>\n",
       "      <td>0.449869</td>\n",
       "      <td>0.449803</td>\n",
       "      <td>0.449736</td>\n",
       "      <td>0.449669</td>\n",
       "      <td>0.449601</td>\n",
       "      <td>0.449462</td>\n",
       "      <td>0.449318</td>\n",
       "      <td>0.449167</td>\n",
       "      <td>0.449010</td>\n",
       "      <td>0.448850</td>\n",
       "      <td>0.448686</td>\n",
       "      <td>0.448519</td>\n",
       "      <td>0.448090</td>\n",
       "      <td>0.447649</td>\n",
       "      <td>0.446749</td>\n",
       "      <td>0.445865</td>\n",
       "      <td>0.444986</td>\n",
       "      <td>0.443692</td>\n",
       "      <td>0.442439</td>\n",
       "      <td>0.441219</td>\n",
       "      <td>0.440029</td>\n",
       "      <td>0.437739</td>\n",
       "      <td>0.435550</td>\n",
       "      <td>0.431502</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.899999</td>\n",
       "      <td>0.899989</td>\n",
       "      <td>0.899978</td>\n",
       "      <td>0.899967</td>\n",
       "      <td>0.899935</td>\n",
       "      <td>0.899902</td>\n",
       "      <td>0.899868</td>\n",
       "      <td>0.899834</td>\n",
       "      <td>0.899800</td>\n",
       "      <td>0.899731</td>\n",
       "      <td>0.899659</td>\n",
       "      <td>0.899583</td>\n",
       "      <td>0.899505</td>\n",
       "      <td>0.899425</td>\n",
       "      <td>0.899343</td>\n",
       "      <td>0.899259</td>\n",
       "      <td>0.899045</td>\n",
       "      <td>0.898825</td>\n",
       "      <td>0.898375</td>\n",
       "      <td>0.897932</td>\n",
       "      <td>0.897493</td>\n",
       "      <td>0.896846</td>\n",
       "      <td>0.896219</td>\n",
       "      <td>0.895609</td>\n",
       "      <td>0.895015</td>\n",
       "      <td>0.893870</td>\n",
       "      <td>0.892775</td>\n",
       "      <td>0.890751</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.089999</td>\n",
       "      <td>0.089978</td>\n",
       "      <td>0.089957</td>\n",
       "      <td>0.089935</td>\n",
       "      <td>0.089870</td>\n",
       "      <td>0.089806</td>\n",
       "      <td>0.089742</td>\n",
       "      <td>0.089679</td>\n",
       "      <td>0.089616</td>\n",
       "      <td>0.089493</td>\n",
       "      <td>0.089373</td>\n",
       "      <td>0.089257</td>\n",
       "      <td>0.089145</td>\n",
       "      <td>0.089036</td>\n",
       "      <td>0.088929</td>\n",
       "      <td>0.088825</td>\n",
       "      <td>0.088573</td>\n",
       "      <td>0.088331</td>\n",
       "      <td>0.087871</td>\n",
       "      <td>0.087421</td>\n",
       "      <td>0.086984</td>\n",
       "      <td>0.086348</td>\n",
       "      <td>0.085725</td>\n",
       "      <td>0.085119</td>\n",
       "      <td>0.084530</td>\n",
       "      <td>0.083396</td>\n",
       "      <td>0.082309</td>\n",
       "      <td>0.080301</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.439052e-08</td>\n",
       "      <td>5.438961e-07</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>0.000639</td>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.001841</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>0.003091</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>0.004245</td>\n",
       "      <td>0.004972</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.785989e-17</td>\n",
       "      <td>9.790417e-14</td>\n",
       "      <td>8.604024e-10</td>\n",
       "      <td>7.029126e-09</td>\n",
       "      <td>2.247480e-08</td>\n",
       "      <td>2.239293e-07</td>\n",
       "      <td>8.322453e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000666</td>\n",
       "      <td>0.000885</td>\n",
       "      <td>0.001095</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0</td>\n",
       "      <td>5.439052e-08</td>\n",
       "      <td>5.438959e-07</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000184</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000434</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.000490</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.299996</td>\n",
       "      <td>0.299926</td>\n",
       "      <td>0.299853</td>\n",
       "      <td>0.299780</td>\n",
       "      <td>0.299561</td>\n",
       "      <td>0.299344</td>\n",
       "      <td>0.299129</td>\n",
       "      <td>0.298914</td>\n",
       "      <td>0.298702</td>\n",
       "      <td>0.298281</td>\n",
       "      <td>0.297865</td>\n",
       "      <td>0.297455</td>\n",
       "      <td>0.297051</td>\n",
       "      <td>0.296651</td>\n",
       "      <td>0.296256</td>\n",
       "      <td>0.295866</td>\n",
       "      <td>0.294912</td>\n",
       "      <td>0.293987</td>\n",
       "      <td>0.292219</td>\n",
       "      <td>0.290553</td>\n",
       "      <td>0.288968</td>\n",
       "      <td>0.286734</td>\n",
       "      <td>0.284668</td>\n",
       "      <td>0.282751</td>\n",
       "      <td>0.280952</td>\n",
       "      <td>0.277650</td>\n",
       "      <td>0.274722</td>\n",
       "      <td>0.269678</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.449996</td>\n",
       "      <td>0.449925</td>\n",
       "      <td>0.449844</td>\n",
       "      <td>0.449756</td>\n",
       "      <td>0.449463</td>\n",
       "      <td>0.449146</td>\n",
       "      <td>0.448827</td>\n",
       "      <td>0.448503</td>\n",
       "      <td>0.448182</td>\n",
       "      <td>0.447551</td>\n",
       "      <td>0.446927</td>\n",
       "      <td>0.446311</td>\n",
       "      <td>0.445702</td>\n",
       "      <td>0.445102</td>\n",
       "      <td>0.444509</td>\n",
       "      <td>0.443923</td>\n",
       "      <td>0.442491</td>\n",
       "      <td>0.441102</td>\n",
       "      <td>0.438448</td>\n",
       "      <td>0.435946</td>\n",
       "      <td>0.433566</td>\n",
       "      <td>0.430213</td>\n",
       "      <td>0.427111</td>\n",
       "      <td>0.424232</td>\n",
       "      <td>0.421531</td>\n",
       "      <td>0.416574</td>\n",
       "      <td>0.412178</td>\n",
       "      <td>0.404605</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.899998</td>\n",
       "      <td>0.899963</td>\n",
       "      <td>0.899922</td>\n",
       "      <td>0.899878</td>\n",
       "      <td>0.899731</td>\n",
       "      <td>0.899573</td>\n",
       "      <td>0.899413</td>\n",
       "      <td>0.899252</td>\n",
       "      <td>0.899091</td>\n",
       "      <td>0.898775</td>\n",
       "      <td>0.898464</td>\n",
       "      <td>0.898155</td>\n",
       "      <td>0.897851</td>\n",
       "      <td>0.897551</td>\n",
       "      <td>0.897254</td>\n",
       "      <td>0.896962</td>\n",
       "      <td>0.896245</td>\n",
       "      <td>0.895551</td>\n",
       "      <td>0.894224</td>\n",
       "      <td>0.892973</td>\n",
       "      <td>0.891783</td>\n",
       "      <td>0.890106</td>\n",
       "      <td>0.888555</td>\n",
       "      <td>0.887116</td>\n",
       "      <td>0.885766</td>\n",
       "      <td>0.883287</td>\n",
       "      <td>0.881089</td>\n",
       "      <td>0.877303</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.089996</td>\n",
       "      <td>0.089927</td>\n",
       "      <td>0.089858</td>\n",
       "      <td>0.089792</td>\n",
       "      <td>0.089610</td>\n",
       "      <td>0.089443</td>\n",
       "      <td>0.089280</td>\n",
       "      <td>0.089120</td>\n",
       "      <td>0.088962</td>\n",
       "      <td>0.088646</td>\n",
       "      <td>0.088334</td>\n",
       "      <td>0.088028</td>\n",
       "      <td>0.087725</td>\n",
       "      <td>0.087425</td>\n",
       "      <td>0.087129</td>\n",
       "      <td>0.086837</td>\n",
       "      <td>0.086122</td>\n",
       "      <td>0.085429</td>\n",
       "      <td>0.084105</td>\n",
       "      <td>0.082856</td>\n",
       "      <td>0.081669</td>\n",
       "      <td>0.079995</td>\n",
       "      <td>0.078447</td>\n",
       "      <td>0.077010</td>\n",
       "      <td>0.075662</td>\n",
       "      <td>0.073188</td>\n",
       "      <td>0.070994</td>\n",
       "      <td>0.067215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.841858e-07</td>\n",
       "      <td>1.841753e-06</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>0.000436</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.001272</td>\n",
       "      <td>0.001475</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.001872</td>\n",
       "      <td>0.002067</td>\n",
       "      <td>0.002544</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.005516</td>\n",
       "      <td>0.006633</td>\n",
       "      <td>0.007666</td>\n",
       "      <td>0.008625</td>\n",
       "      <td>0.009524</td>\n",
       "      <td>0.011175</td>\n",
       "      <td>0.012639</td>\n",
       "      <td>0.015161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.089422e-14</td>\n",
       "      <td>6.221371e-11</td>\n",
       "      <td>6.345060e-07</td>\n",
       "      <td>4.757331e-06</td>\n",
       "      <td>1.211407e-05</td>\n",
       "      <td>4.917962e-05</td>\n",
       "      <td>9.914453e-05</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.000365</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.000774</td>\n",
       "      <td>0.000873</td>\n",
       "      <td>0.000971</td>\n",
       "      <td>0.001210</td>\n",
       "      <td>0.001442</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>0.002303</td>\n",
       "      <td>0.002701</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.003779</td>\n",
       "      <td>0.004259</td>\n",
       "      <td>0.004710</td>\n",
       "      <td>0.005538</td>\n",
       "      <td>0.006272</td>\n",
       "      <td>0.007536</td>\n",
       "      <td>0</td>\n",
       "      <td>1.841857e-07</td>\n",
       "      <td>1.841629e-06</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.000103</td>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299989</td>\n",
       "      <td>0.299887</td>\n",
       "      <td>0.297811</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.293877</td>\n",
       "      <td>0.288868</td>\n",
       "      <td>0.284657</td>\n",
       "      <td>0.281052</td>\n",
       "      <td>0.277897</td>\n",
       "      <td>0.275140</td>\n",
       "      <td>0.270396</td>\n",
       "      <td>0.266510</td>\n",
       "      <td>0.263205</td>\n",
       "      <td>0.260369</td>\n",
       "      <td>0.257853</td>\n",
       "      <td>0.255641</td>\n",
       "      <td>0.253642</td>\n",
       "      <td>0.249390</td>\n",
       "      <td>0.245906</td>\n",
       "      <td>0.240458</td>\n",
       "      <td>0.236294</td>\n",
       "      <td>0.232899</td>\n",
       "      <td>0.228847</td>\n",
       "      <td>0.225618</td>\n",
       "      <td>0.222920</td>\n",
       "      <td>0.220661</td>\n",
       "      <td>0.216967</td>\n",
       "      <td>0.214089</td>\n",
       "      <td>0.209784</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.449989</td>\n",
       "      <td>0.449887</td>\n",
       "      <td>0.447811</td>\n",
       "      <td>0.445773</td>\n",
       "      <td>0.443872</td>\n",
       "      <td>0.438833</td>\n",
       "      <td>0.434559</td>\n",
       "      <td>0.430853</td>\n",
       "      <td>0.427558</td>\n",
       "      <td>0.424627</td>\n",
       "      <td>0.419436</td>\n",
       "      <td>0.415000</td>\n",
       "      <td>0.411066</td>\n",
       "      <td>0.407545</td>\n",
       "      <td>0.404307</td>\n",
       "      <td>0.401347</td>\n",
       "      <td>0.398590</td>\n",
       "      <td>0.392440</td>\n",
       "      <td>0.387118</td>\n",
       "      <td>0.378287</td>\n",
       "      <td>0.371179</td>\n",
       "      <td>0.365241</td>\n",
       "      <td>0.357978</td>\n",
       "      <td>0.352112</td>\n",
       "      <td>0.347187</td>\n",
       "      <td>0.343042</td>\n",
       "      <td>0.336265</td>\n",
       "      <td>0.330984</td>\n",
       "      <td>0.323111</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.899994</td>\n",
       "      <td>0.899943</td>\n",
       "      <td>0.898906</td>\n",
       "      <td>0.897886</td>\n",
       "      <td>0.896936</td>\n",
       "      <td>0.894417</td>\n",
       "      <td>0.892279</td>\n",
       "      <td>0.890426</td>\n",
       "      <td>0.888779</td>\n",
       "      <td>0.887313</td>\n",
       "      <td>0.884718</td>\n",
       "      <td>0.882500</td>\n",
       "      <td>0.880533</td>\n",
       "      <td>0.878772</td>\n",
       "      <td>0.877154</td>\n",
       "      <td>0.875673</td>\n",
       "      <td>0.874295</td>\n",
       "      <td>0.871220</td>\n",
       "      <td>0.868559</td>\n",
       "      <td>0.864143</td>\n",
       "      <td>0.860590</td>\n",
       "      <td>0.857620</td>\n",
       "      <td>0.853989</td>\n",
       "      <td>0.851056</td>\n",
       "      <td>0.848593</td>\n",
       "      <td>0.846521</td>\n",
       "      <td>0.843132</td>\n",
       "      <td>0.840492</td>\n",
       "      <td>0.836555</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.089989</td>\n",
       "      <td>0.089887</td>\n",
       "      <td>0.087811</td>\n",
       "      <td>0.085776</td>\n",
       "      <td>0.083880</td>\n",
       "      <td>0.078885</td>\n",
       "      <td>0.074706</td>\n",
       "      <td>0.071152</td>\n",
       "      <td>0.068066</td>\n",
       "      <td>0.065397</td>\n",
       "      <td>0.060877</td>\n",
       "      <td>0.057265</td>\n",
       "      <td>0.054275</td>\n",
       "      <td>0.051782</td>\n",
       "      <td>0.049625</td>\n",
       "      <td>0.047788</td>\n",
       "      <td>0.046168</td>\n",
       "      <td>0.042865</td>\n",
       "      <td>0.040301</td>\n",
       "      <td>0.036544</td>\n",
       "      <td>0.033852</td>\n",
       "      <td>0.031728</td>\n",
       "      <td>0.029282</td>\n",
       "      <td>0.027372</td>\n",
       "      <td>0.025787</td>\n",
       "      <td>0.024470</td>\n",
       "      <td>0.022318</td>\n",
       "      <td>0.020641</td>\n",
       "      <td>0.018121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.681865e-06</td>\n",
       "      <td>5.671879e-05</td>\n",
       "      <td>0.001094</td>\n",
       "      <td>0.002113</td>\n",
       "      <td>0.003061</td>\n",
       "      <td>0.005566</td>\n",
       "      <td>0.007672</td>\n",
       "      <td>0.009474</td>\n",
       "      <td>0.011052</td>\n",
       "      <td>0.012430</td>\n",
       "      <td>0.014802</td>\n",
       "      <td>0.016745</td>\n",
       "      <td>0.018397</td>\n",
       "      <td>0.019815</td>\n",
       "      <td>0.021074</td>\n",
       "      <td>0.022180</td>\n",
       "      <td>0.023179</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>0.027047</td>\n",
       "      <td>0.029771</td>\n",
       "      <td>0.031853</td>\n",
       "      <td>0.033550</td>\n",
       "      <td>0.035576</td>\n",
       "      <td>0.037191</td>\n",
       "      <td>0.038540</td>\n",
       "      <td>0.039670</td>\n",
       "      <td>0.041516</td>\n",
       "      <td>0.042956</td>\n",
       "      <td>0.045108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.414843e-14</td>\n",
       "      <td>1.460929e-11</td>\n",
       "      <td>1.257166e-07</td>\n",
       "      <td>9.654730e-07</td>\n",
       "      <td>2.777487e-06</td>\n",
       "      <td>1.725415e-05</td>\n",
       "      <td>4.904515e-05</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.001070</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>0.001773</td>\n",
       "      <td>0.002147</td>\n",
       "      <td>0.002526</td>\n",
       "      <td>0.003475</td>\n",
       "      <td>0.004394</td>\n",
       "      <td>0.006086</td>\n",
       "      <td>0.007558</td>\n",
       "      <td>0.008829</td>\n",
       "      <td>0.010434</td>\n",
       "      <td>0.011753</td>\n",
       "      <td>0.012867</td>\n",
       "      <td>0.013809</td>\n",
       "      <td>0.015351</td>\n",
       "      <td>0.016552</td>\n",
       "      <td>0.018337</td>\n",
       "      <td>0</td>\n",
       "      <td>5.681865e-06</td>\n",
       "      <td>5.671876e-05</td>\n",
       "      <td>0.001094</td>\n",
       "      <td>0.002111</td>\n",
       "      <td>0.003056</td>\n",
       "      <td>0.005532</td>\n",
       "      <td>0.007574</td>\n",
       "      <td>0.009275</td>\n",
       "      <td>0.010713</td>\n",
       "      <td>0.011916</td>\n",
       "      <td>0.013842</td>\n",
       "      <td>0.015235</td>\n",
       "      <td>0.016258</td>\n",
       "      <td>0.016991</td>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.017886</td>\n",
       "      <td>0.018127</td>\n",
       "      <td>0.018355</td>\n",
       "      <td>0.018259</td>\n",
       "      <td>0.017600</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.015892</td>\n",
       "      <td>0.014708</td>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.012807</td>\n",
       "      <td>0.012051</td>\n",
       "      <td>0.010814</td>\n",
       "      <td>0.009851</td>\n",
       "      <td>0.008435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.299941</td>\n",
       "      <td>0.299417</td>\n",
       "      <td>0.290150</td>\n",
       "      <td>0.282960</td>\n",
       "      <td>0.277360</td>\n",
       "      <td>0.265672</td>\n",
       "      <td>0.257948</td>\n",
       "      <td>0.252221</td>\n",
       "      <td>0.247715</td>\n",
       "      <td>0.244016</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.233758</td>\n",
       "      <td>0.230221</td>\n",
       "      <td>0.227279</td>\n",
       "      <td>0.224805</td>\n",
       "      <td>0.222652</td>\n",
       "      <td>0.220794</td>\n",
       "      <td>0.216952</td>\n",
       "      <td>0.213972</td>\n",
       "      <td>0.209558</td>\n",
       "      <td>0.206398</td>\n",
       "      <td>0.203963</td>\n",
       "      <td>0.201194</td>\n",
       "      <td>0.199118</td>\n",
       "      <td>0.197448</td>\n",
       "      <td>0.196118</td>\n",
       "      <td>0.194017</td>\n",
       "      <td>0.192473</td>\n",
       "      <td>0.190304</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.449941</td>\n",
       "      <td>0.449417</td>\n",
       "      <td>0.440063</td>\n",
       "      <td>0.432463</td>\n",
       "      <td>0.426106</td>\n",
       "      <td>0.410992</td>\n",
       "      <td>0.399513</td>\n",
       "      <td>0.390405</td>\n",
       "      <td>0.382988</td>\n",
       "      <td>0.376805</td>\n",
       "      <td>0.366985</td>\n",
       "      <td>0.359449</td>\n",
       "      <td>0.353445</td>\n",
       "      <td>0.348458</td>\n",
       "      <td>0.344271</td>\n",
       "      <td>0.340633</td>\n",
       "      <td>0.337498</td>\n",
       "      <td>0.331030</td>\n",
       "      <td>0.326029</td>\n",
       "      <td>0.318647</td>\n",
       "      <td>0.313381</td>\n",
       "      <td>0.309333</td>\n",
       "      <td>0.304743</td>\n",
       "      <td>0.301309</td>\n",
       "      <td>0.298551</td>\n",
       "      <td>0.296359</td>\n",
       "      <td>0.292899</td>\n",
       "      <td>0.290363</td>\n",
       "      <td>0.286804</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.899971</td>\n",
       "      <td>0.899709</td>\n",
       "      <td>0.895032</td>\n",
       "      <td>0.891231</td>\n",
       "      <td>0.888053</td>\n",
       "      <td>0.880496</td>\n",
       "      <td>0.874756</td>\n",
       "      <td>0.870203</td>\n",
       "      <td>0.866494</td>\n",
       "      <td>0.863403</td>\n",
       "      <td>0.858493</td>\n",
       "      <td>0.854725</td>\n",
       "      <td>0.851723</td>\n",
       "      <td>0.849229</td>\n",
       "      <td>0.847135</td>\n",
       "      <td>0.845316</td>\n",
       "      <td>0.843749</td>\n",
       "      <td>0.840515</td>\n",
       "      <td>0.838014</td>\n",
       "      <td>0.834324</td>\n",
       "      <td>0.831690</td>\n",
       "      <td>0.829667</td>\n",
       "      <td>0.827372</td>\n",
       "      <td>0.825654</td>\n",
       "      <td>0.824275</td>\n",
       "      <td>0.823180</td>\n",
       "      <td>0.821450</td>\n",
       "      <td>0.820182</td>\n",
       "      <td>0.818402</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.089941</td>\n",
       "      <td>0.089417</td>\n",
       "      <td>0.080194</td>\n",
       "      <td>0.073208</td>\n",
       "      <td>0.067988</td>\n",
       "      <td>0.058012</td>\n",
       "      <td>0.052166</td>\n",
       "      <td>0.048128</td>\n",
       "      <td>0.045078</td>\n",
       "      <td>0.042621</td>\n",
       "      <td>0.038807</td>\n",
       "      <td>0.035913</td>\n",
       "      <td>0.033609</td>\n",
       "      <td>0.031689</td>\n",
       "      <td>0.030073</td>\n",
       "      <td>0.028662</td>\n",
       "      <td>0.027442</td>\n",
       "      <td>0.024913</td>\n",
       "      <td>0.022943</td>\n",
       "      <td>0.020014</td>\n",
       "      <td>0.017906</td>\n",
       "      <td>0.016277</td>\n",
       "      <td>0.014420</td>\n",
       "      <td>0.013022</td>\n",
       "      <td>0.011896</td>\n",
       "      <td>0.010998</td>\n",
       "      <td>0.009576</td>\n",
       "      <td>0.008529</td>\n",
       "      <td>0.007053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.939995e-05</td>\n",
       "      <td>2.913540e-04</td>\n",
       "      <td>0.004925</td>\n",
       "      <td>0.008520</td>\n",
       "      <td>0.011320</td>\n",
       "      <td>0.017164</td>\n",
       "      <td>0.021026</td>\n",
       "      <td>0.023890</td>\n",
       "      <td>0.026143</td>\n",
       "      <td>0.027992</td>\n",
       "      <td>0.030900</td>\n",
       "      <td>0.033121</td>\n",
       "      <td>0.034889</td>\n",
       "      <td>0.036360</td>\n",
       "      <td>0.037597</td>\n",
       "      <td>0.038674</td>\n",
       "      <td>0.039603</td>\n",
       "      <td>0.041524</td>\n",
       "      <td>0.043014</td>\n",
       "      <td>0.045221</td>\n",
       "      <td>0.046801</td>\n",
       "      <td>0.048019</td>\n",
       "      <td>0.049403</td>\n",
       "      <td>0.050441</td>\n",
       "      <td>0.051276</td>\n",
       "      <td>0.051941</td>\n",
       "      <td>0.052992</td>\n",
       "      <td>0.053763</td>\n",
       "      <td>0.054848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.615493e-12</td>\n",
       "      <td>8.619145e-09</td>\n",
       "      <td>4.356465e-05</td>\n",
       "      <td>2.484761e-04</td>\n",
       "      <td>6.272497e-04</td>\n",
       "      <td>2.340022e-03</td>\n",
       "      <td>4.217780e-03</td>\n",
       "      <td>0.005908</td>\n",
       "      <td>0.007363</td>\n",
       "      <td>0.008605</td>\n",
       "      <td>0.010607</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.013388</td>\n",
       "      <td>0.014410</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.016010</td>\n",
       "      <td>0.016648</td>\n",
       "      <td>0.017961</td>\n",
       "      <td>0.018972</td>\n",
       "      <td>0.020455</td>\n",
       "      <td>0.021508</td>\n",
       "      <td>0.022315</td>\n",
       "      <td>0.023226</td>\n",
       "      <td>0.023905</td>\n",
       "      <td>0.024449</td>\n",
       "      <td>0.024880</td>\n",
       "      <td>0.025559</td>\n",
       "      <td>0.026055</td>\n",
       "      <td>0.026750</td>\n",
       "      <td>0</td>\n",
       "      <td>2.939994e-05</td>\n",
       "      <td>2.913368e-04</td>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.008023</td>\n",
       "      <td>0.010065</td>\n",
       "      <td>0.012484</td>\n",
       "      <td>0.012590</td>\n",
       "      <td>0.012074</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>0.010782</td>\n",
       "      <td>0.009685</td>\n",
       "      <td>0.008812</td>\n",
       "      <td>0.008113</td>\n",
       "      <td>0.007540</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>0.006654</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.005602</td>\n",
       "      <td>0.005071</td>\n",
       "      <td>0.004310</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.003389</td>\n",
       "      <td>0.002952</td>\n",
       "      <td>0.002632</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002182</td>\n",
       "      <td>0.001874</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>0.001348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0     0.3  0.299999  0.299992  0.299837  0.299675  0.299514  0.299035   \n",
       "1     0.3  0.300000  0.299999  0.299978  0.299957  0.299935  0.299870   \n",
       "2     0.3  0.300000  0.299996  0.299926  0.299853  0.299780  0.299561   \n",
       "3     0.3  0.299989  0.299887  0.297811  0.295775  0.293877  0.288868   \n",
       "4     0.3  0.299941  0.299417  0.290150  0.282960  0.277360  0.265672   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  cSM_360s  cSM_480s  cSM_600s  cSM_720s  \\\n",
       "0  0.298564  0.298101  0.297645  0.297197  0.296321  0.295471  0.294644   \n",
       "1  0.299805  0.299740  0.299675  0.299611  0.299482  0.299355  0.299227   \n",
       "2  0.299344  0.299129  0.298914  0.298702  0.298281  0.297865  0.297455   \n",
       "3  0.284657  0.281052  0.277897  0.275140  0.270396  0.266510  0.263205   \n",
       "4  0.257948  0.252221  0.247715  0.244016  0.238200  0.233758  0.230221   \n",
       "\n",
       "   cSM_840s  cSM_960s  cSM_1080s  cSM_1200s  cSM_1500s  cSM_1800s  cSM_2400s  \\\n",
       "0  0.293842  0.293062   0.292304   0.291567   0.289810   0.288156   0.285124   \n",
       "1  0.299100  0.298974   0.298848   0.298723   0.298412   0.298104   0.297497   \n",
       "2  0.297051  0.296651   0.296256   0.295866   0.294912   0.293987   0.292219   \n",
       "3  0.260369  0.257853   0.255641   0.253642   0.249390   0.245906   0.240458   \n",
       "4  0.227279  0.224805   0.222652   0.220794   0.216952   0.213972   0.209558   \n",
       "\n",
       "   cSM_3000s  cSM_3600s  cSM_4500s  cSM_5400s  cSM_6300s  cSM_7200s  \\\n",
       "0   0.282405   0.279936   0.276626   0.273681   0.271057   0.268655   \n",
       "1   0.296902   0.296318   0.295462   0.294630   0.293819   0.293030   \n",
       "2   0.290553   0.288968   0.286734   0.284668   0.282751   0.280952   \n",
       "3   0.236294   0.232899   0.228847   0.225618   0.222920   0.220661   \n",
       "4   0.206398   0.203963   0.201194   0.199118   0.197448   0.196118   \n",
       "\n",
       "   cSM_9000s  cSM_10800s  cSM_14400s  cR_0s   cR_0.1s     cR_1s    cR_20s  \\\n",
       "0   0.264483    0.260906    0.255077   0.45  0.449999  0.449992  0.449837   \n",
       "1   0.291510    0.290056    0.287368   0.45  0.450000  0.449999  0.449978   \n",
       "2   0.277650    0.274722    0.269678   0.45  0.450000  0.449996  0.449925   \n",
       "3   0.216967    0.214089    0.209784   0.45  0.449989  0.449887  0.447811   \n",
       "4   0.194017    0.192473    0.190304   0.45  0.449941  0.449417  0.440063   \n",
       "\n",
       "     cR_40s    cR_60s   cR_120s   cR_180s   cR_240s   cR_300s   cR_360s  \\\n",
       "0  0.449675  0.449513  0.449035  0.448563  0.448099  0.447641  0.447189   \n",
       "1  0.449957  0.449935  0.449869  0.449803  0.449736  0.449669  0.449601   \n",
       "2  0.449844  0.449756  0.449463  0.449146  0.448827  0.448503  0.448182   \n",
       "3  0.445773  0.443872  0.438833  0.434559  0.430853  0.427558  0.424627   \n",
       "4  0.432463  0.426106  0.410992  0.399513  0.390405  0.382988  0.376805   \n",
       "\n",
       "    cR_480s   cR_600s   cR_720s   cR_840s   cR_960s  cR_1080s  cR_1200s  \\\n",
       "0  0.446304  0.445441  0.444591  0.443759  0.442944  0.442143  0.441357   \n",
       "1  0.449462  0.449318  0.449167  0.449010  0.448850  0.448686  0.448519   \n",
       "2  0.447551  0.446927  0.446311  0.445702  0.445102  0.444509  0.443923   \n",
       "3  0.419436  0.415000  0.411066  0.407545  0.404307  0.401347  0.398590   \n",
       "4  0.366985  0.359449  0.353445  0.348458  0.344271  0.340633  0.337498   \n",
       "\n",
       "   cR_1500s  cR_1800s  cR_2400s  cR_3000s  cR_3600s  cR_4500s  cR_5400s  \\\n",
       "0  0.439447  0.437585  0.434001  0.430589  0.427321  0.422705  0.418420   \n",
       "1  0.448090  0.447649  0.446749  0.445865  0.444986  0.443692  0.442439   \n",
       "2  0.442491  0.441102  0.438448  0.435946  0.433566  0.430213  0.427111   \n",
       "3  0.392440  0.387118  0.378287  0.371179  0.365241  0.357978  0.352112   \n",
       "4  0.331030  0.326029  0.318647  0.313381  0.309333  0.304743  0.301309   \n",
       "\n",
       "   cR_6300s  cR_7200s  cR_9000s  cR_10800s  cR_14400s  cB_0s   cB_0.1s  \\\n",
       "0  0.414446  0.410773  0.404216   0.398530   0.389175    0.9  0.900000   \n",
       "1  0.441219  0.440029  0.437739   0.435550   0.431502    0.9  0.900000   \n",
       "2  0.424232  0.421531  0.416574   0.412178   0.404605    0.9  0.900000   \n",
       "3  0.347187  0.343042  0.336265   0.330984   0.323111    0.9  0.899994   \n",
       "4  0.298551  0.296359  0.292899   0.290363   0.286804    0.9  0.899971   \n",
       "\n",
       "      cB_1s    cB_20s    cB_40s    cB_60s   cB_120s   cB_180s   cB_240s  \\\n",
       "0  0.899996  0.899918  0.899837  0.899757  0.899517  0.899282  0.899049   \n",
       "1  0.899999  0.899989  0.899978  0.899967  0.899935  0.899902  0.899868   \n",
       "2  0.899998  0.899963  0.899922  0.899878  0.899731  0.899573  0.899413   \n",
       "3  0.899943  0.898906  0.897886  0.896936  0.894417  0.892279  0.890426   \n",
       "4  0.899709  0.895032  0.891231  0.888053  0.880496  0.874756  0.870203   \n",
       "\n",
       "    cB_300s   cB_360s   cB_480s   cB_600s   cB_720s   cB_840s   cB_960s  \\\n",
       "0  0.898820  0.898595  0.898152  0.897720  0.897296  0.896880  0.896472   \n",
       "1  0.899834  0.899800  0.899731  0.899659  0.899583  0.899505  0.899425   \n",
       "2  0.899252  0.899091  0.898775  0.898464  0.898155  0.897851  0.897551   \n",
       "3  0.888779  0.887313  0.884718  0.882500  0.880533  0.878772  0.877154   \n",
       "4  0.866494  0.863403  0.858493  0.854725  0.851723  0.849229  0.847135   \n",
       "\n",
       "   cB_1080s  cB_1200s  cB_1500s  cB_1800s  cB_2400s  cB_3000s  cB_3600s  \\\n",
       "0  0.896072  0.895679  0.894724  0.893792  0.892001  0.890294  0.888661   \n",
       "1  0.899343  0.899259  0.899045  0.898825  0.898375  0.897932  0.897493   \n",
       "2  0.897254  0.896962  0.896245  0.895551  0.894224  0.892973  0.891783   \n",
       "3  0.875673  0.874295  0.871220  0.868559  0.864143  0.860590  0.857620   \n",
       "4  0.845316  0.843749  0.840515  0.838014  0.834324  0.831690  0.829667   \n",
       "\n",
       "   cB_4500s  cB_5400s  cB_6300s  cB_7200s  cB_9000s  cB_10800s  cB_14400s  \\\n",
       "0  0.886353  0.884210  0.882223  0.880387  0.877108   0.874265   0.869588   \n",
       "1  0.896846  0.896219  0.895609  0.895015  0.893870   0.892775   0.890751   \n",
       "2  0.890106  0.888555  0.887116  0.885766  0.883287   0.881089   0.877303   \n",
       "3  0.853989  0.851056  0.848593  0.846521  0.843132   0.840492   0.836555   \n",
       "4  0.827372  0.825654  0.824275  0.823180  0.821450   0.820182   0.818402   \n",
       "\n",
       "   cC_0s   cC_0.1s     cC_1s    cC_20s    cC_40s    cC_60s   cC_120s  \\\n",
       "0   0.09  0.089999  0.089992  0.089837  0.089675  0.089514  0.089035   \n",
       "1   0.09  0.090000  0.089999  0.089978  0.089957  0.089935  0.089870   \n",
       "2   0.09  0.090000  0.089996  0.089927  0.089858  0.089792  0.089610   \n",
       "3   0.09  0.089989  0.089887  0.087811  0.085776  0.083880  0.078885   \n",
       "4   0.09  0.089941  0.089417  0.080194  0.073208  0.067988  0.058012   \n",
       "\n",
       "    cC_180s   cC_240s   cC_300s   cC_360s   cC_480s   cC_600s   cC_720s  \\\n",
       "0  0.088565  0.088102  0.087648  0.087201  0.086329  0.085486  0.084671   \n",
       "1  0.089806  0.089742  0.089679  0.089616  0.089493  0.089373  0.089257   \n",
       "2  0.089443  0.089280  0.089120  0.088962  0.088646  0.088334  0.088028   \n",
       "3  0.074706  0.071152  0.068066  0.065397  0.060877  0.057265  0.054275   \n",
       "4  0.052166  0.048128  0.045078  0.042621  0.038807  0.035913  0.033609   \n",
       "\n",
       "    cC_840s   cC_960s  cC_1080s  cC_1200s  cC_1500s  cC_1800s  cC_2400s  \\\n",
       "0  0.083883  0.083121  0.082384  0.081672  0.079992  0.078441  0.075686   \n",
       "1  0.089145  0.089036  0.088929  0.088825  0.088573  0.088331  0.087871   \n",
       "2  0.087725  0.087425  0.087129  0.086837  0.086122  0.085429  0.084105   \n",
       "3  0.051782  0.049625  0.047788  0.046168  0.042865  0.040301  0.036544   \n",
       "4  0.031689  0.030073  0.028662  0.027442  0.024913  0.022943  0.020014   \n",
       "\n",
       "   cC_3000s  cC_3600s  cC_4500s  cC_5400s  cC_6300s  cC_7200s  cC_9000s  \\\n",
       "0  0.073313  0.071243  0.068586  0.066312  0.064363  0.062596  0.059617   \n",
       "1  0.087421  0.086984  0.086348  0.085725  0.085119  0.084530  0.083396   \n",
       "2  0.082856  0.081669  0.079995  0.078447  0.077010  0.075662  0.073188   \n",
       "3  0.033852  0.031728  0.029282  0.027372  0.025787  0.024470  0.022318   \n",
       "4  0.017906  0.016277  0.014420  0.013022  0.011896  0.010998  0.009576   \n",
       "\n",
       "   cC_10800s  cC_14400s  cP_0s       cP_0.1s         cP_1s    cP_20s  \\\n",
       "0   0.057093   0.053027    0.0  4.088057e-07  4.087539e-06  0.000082   \n",
       "1   0.082309   0.080301    0.0  5.439052e-08  5.438961e-07  0.000011   \n",
       "2   0.070994   0.067215    0.0  1.841858e-07  1.841753e-06  0.000037   \n",
       "3   0.020641   0.018121    0.0  5.681865e-06  5.671879e-05  0.001094   \n",
       "4   0.008529   0.007053    0.0  2.939995e-05  2.913540e-04  0.004925   \n",
       "\n",
       "     cP_40s    cP_60s   cP_120s   cP_180s   cP_240s   cP_300s   cP_360s  \\\n",
       "0  0.000163  0.000243  0.000482  0.000718  0.000949  0.001177  0.001402   \n",
       "1  0.000022  0.000033  0.000065  0.000098  0.000130  0.000162  0.000194   \n",
       "2  0.000073  0.000110  0.000219  0.000328  0.000436  0.000543  0.000649   \n",
       "3  0.002113  0.003061  0.005566  0.007672  0.009474  0.011052  0.012430   \n",
       "4  0.008520  0.011320  0.017164  0.021026  0.023890  0.026143  0.027992   \n",
       "\n",
       "    cP_480s   cP_600s   cP_720s   cP_840s   cP_960s  cP_1080s  cP_1200s  \\\n",
       "0  0.001840  0.002264  0.002678  0.003079  0.003469  0.003848  0.004217   \n",
       "1  0.000259  0.000323  0.000386  0.000450  0.000513  0.000576  0.000639   \n",
       "2  0.000860  0.001067  0.001272  0.001475  0.001675  0.001872  0.002067   \n",
       "3  0.014802  0.016745  0.018397  0.019815  0.021074  0.022180  0.023179   \n",
       "4  0.030900  0.033121  0.034889  0.036360  0.037597  0.038674  0.039603   \n",
       "\n",
       "   cP_1500s  cP_1800s  cP_2400s  cP_3000s  cP_3600s  cP_4500s  cP_5400s  \\\n",
       "0  0.005095  0.005922  0.007438  0.008797  0.010032  0.011687  0.013159   \n",
       "1  0.000794  0.000948  0.001251  0.001549  0.001841  0.002269  0.002685   \n",
       "2  0.002544  0.003007  0.003891  0.004724  0.005516  0.006633  0.007666   \n",
       "3  0.025305  0.027047  0.029771  0.031853  0.033550  0.035576  0.037191   \n",
       "4  0.041524  0.043014  0.045221  0.046801  0.048019  0.049403  0.050441   \n",
       "\n",
       "   cP_6300s  cP_7200s  cP_9000s  cP_10800s  cP_14400s  cIMP1_0s  cIMP1_0.1s  \\\n",
       "0  0.014471  0.015673  0.017758   0.019547   0.022462       0.0         0.0   \n",
       "1  0.003091  0.003485  0.004245   0.004972   0.006316       0.0         0.0   \n",
       "2  0.008625  0.009524  0.011175   0.012639   0.015161       0.0         0.0   \n",
       "3  0.038540  0.039670  0.041516   0.042956   0.045108       0.0         0.0   \n",
       "4  0.051276  0.051941  0.052992   0.053763   0.054848       0.0         0.0   \n",
       "\n",
       "   cIMP1_1s  cIMP1_20s  cIMP1_40s  cIMP1_60s  cIMP1_120s  cIMP1_180s  \\\n",
       "0       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "1       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "2       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "3       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "4       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "\n",
       "   cIMP1_240s  cIMP1_300s  cIMP1_360s  cIMP1_480s  cIMP1_600s  cIMP1_720s  \\\n",
       "0         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   cIMP1_840s  cIMP1_960s  cIMP1_1080s  cIMP1_1200s  cIMP1_1500s  cIMP1_1800s  \\\n",
       "0         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "1         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "2         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "3         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "4         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   cIMP1_2400s  cIMP1_3000s  cIMP1_3600s  cIMP1_4500s  cIMP1_5400s  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   cIMP1_6300s  cIMP1_7200s  cIMP1_9000s  cIMP1_10800s  cIMP1_14400s  \\\n",
       "0          0.0          0.0          0.0           0.0           0.0   \n",
       "1          0.0          0.0          0.0           0.0           0.0   \n",
       "2          0.0          0.0          0.0           0.0           0.0   \n",
       "3          0.0          0.0          0.0           0.0           0.0   \n",
       "4          0.0          0.0          0.0           0.0           0.0   \n",
       "\n",
       "   cIMP2_0s    cIMP2_0.1s      cIMP2_1s     cIMP2_20s     cIMP2_40s  \\\n",
       "0         0  8.070481e-17  8.090876e-14  6.970219e-10  5.645935e-09   \n",
       "1         0  9.785989e-17  9.790417e-14  8.604024e-10  7.029126e-09   \n",
       "2         0  6.089422e-14  6.221371e-11  6.345060e-07  4.757331e-06   \n",
       "3         0  1.414843e-14  1.460929e-11  1.257166e-07  9.654730e-07   \n",
       "4         0  7.615493e-12  8.619145e-09  4.356465e-05  2.484761e-04   \n",
       "\n",
       "      cIMP2_60s    cIMP2_120s    cIMP2_180s  cIMP2_240s  cIMP2_300s  \\\n",
       "0  1.811532e-08  1.627378e-07  5.814287e-07    0.000001    0.000002   \n",
       "1  2.247480e-08  2.239293e-07  8.322453e-07    0.000002    0.000003   \n",
       "2  1.211407e-05  4.917962e-05  9.914453e-05    0.000151    0.000206   \n",
       "3  2.777487e-06  1.725415e-05  4.904515e-05    0.000100    0.000170   \n",
       "4  6.272497e-04  2.340022e-03  4.217780e-03    0.005908    0.007363   \n",
       "\n",
       "   cIMP2_360s  cIMP2_480s  cIMP2_600s  cIMP2_720s  cIMP2_840s  cIMP2_960s  \\\n",
       "0    0.000004    0.000008    0.000015    0.000027    0.000041    0.000059   \n",
       "1    0.000005    0.000010    0.000018    0.000030    0.000045    0.000062   \n",
       "2    0.000260    0.000365    0.000469    0.000572    0.000674    0.000774   \n",
       "3    0.000257    0.000480    0.000755    0.001070    0.001412    0.001773   \n",
       "4    0.008605    0.010607    0.012154    0.013388    0.014410    0.015267   \n",
       "\n",
       "   cIMP2_1080s  cIMP2_1200s  cIMP2_1500s  cIMP2_1800s  cIMP2_2400s  \\\n",
       "0     0.000080     0.000105     0.000182     0.000285     0.000562   \n",
       "1     0.000081     0.000102     0.000161     0.000227     0.000374   \n",
       "2     0.000873     0.000971     0.001210     0.001442     0.001886   \n",
       "3     0.002147     0.002526     0.003475     0.004394     0.006086   \n",
       "4     0.016010     0.016648     0.017961     0.018972     0.020455   \n",
       "\n",
       "   cIMP2_3000s  cIMP2_3600s  cIMP2_4500s  cIMP2_5400s  cIMP2_6300s  \\\n",
       "0     0.000908     0.001307     0.001960     0.002631     0.003305   \n",
       "1     0.000519     0.000666     0.000885     0.001095     0.001300   \n",
       "2     0.002303     0.002701     0.003261     0.003779     0.004259   \n",
       "3     0.007558     0.008829     0.010434     0.011753     0.012867   \n",
       "4     0.021508     0.022315     0.023226     0.023905     0.024449   \n",
       "\n",
       "   cIMP2_7200s  cIMP2_9000s  cIMP2_10800s  cIMP2_14400s  cINT1_0s  \\\n",
       "0     0.003941     0.005134      0.006188      0.007951         0   \n",
       "1     0.001500     0.001885      0.002253      0.002933         0   \n",
       "2     0.004710     0.005538      0.006272      0.007536         0   \n",
       "3     0.013809     0.015351      0.016552      0.018337         0   \n",
       "4     0.024880     0.025559      0.026055      0.026750         0   \n",
       "\n",
       "     cINT1_0.1s      cINT1_1s  cINT1_20s  cINT1_40s  cINT1_60s  cINT1_120s  \\\n",
       "0  4.088057e-07  4.087539e-06   0.000082   0.000163   0.000243    0.000482   \n",
       "1  5.439052e-08  5.438959e-07   0.000011   0.000022   0.000033    0.000065   \n",
       "2  1.841857e-07  1.841629e-06   0.000036   0.000064   0.000086    0.000121   \n",
       "3  5.681865e-06  5.671876e-05   0.001094   0.002111   0.003056    0.005532   \n",
       "4  2.939994e-05  2.913368e-04   0.004838   0.008023   0.010065    0.012484   \n",
       "\n",
       "   cINT1_180s  cINT1_240s  cINT1_300s  cINT1_360s  cINT1_480s  cINT1_600s  \\\n",
       "0    0.000717    0.000947    0.001173    0.001394    0.001823    0.002234   \n",
       "1    0.000096    0.000126    0.000156    0.000184    0.000238    0.000286   \n",
       "2    0.000130    0.000134    0.000132    0.000129    0.000129    0.000129   \n",
       "3    0.007574    0.009275    0.010713    0.011916    0.013842    0.015235   \n",
       "4    0.012590    0.012074    0.011416    0.010782    0.009685    0.008812   \n",
       "\n",
       "   cINT1_720s  cINT1_840s  cINT1_960s  cINT1_1080s  cINT1_1200s  cINT1_1500s  \\\n",
       "0    0.002625    0.002997    0.003351     0.003688     0.004007     0.004732   \n",
       "1    0.000326    0.000360    0.000389     0.000414     0.000434     0.000472   \n",
       "2    0.000128    0.000126    0.000126     0.000125     0.000125     0.000123   \n",
       "3    0.016258    0.016991    0.017528     0.017886     0.018127     0.018355   \n",
       "4    0.008113    0.007540    0.007063     0.006654     0.006306     0.005602   \n",
       "\n",
       "   cINT1_1800s  cINT1_2400s  cINT1_3000s  cINT1_3600s  cINT1_4500s  \\\n",
       "0     0.005351     0.006314     0.006981     0.007418     0.007767   \n",
       "1     0.000494     0.000503     0.000512     0.000509     0.000498   \n",
       "2     0.000122     0.000119     0.000117     0.000115     0.000111   \n",
       "3     0.018259     0.017600     0.016738     0.015892     0.014708   \n",
       "4     0.005071     0.004310     0.003784     0.003389     0.002952   \n",
       "\n",
       "   cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  cINT1_10800s  \\\n",
       "0     0.007898     0.007860     0.007791     0.007491      0.007172   \n",
       "1     0.000495     0.000490     0.000485     0.000474      0.000466   \n",
       "2     0.000108     0.000106     0.000103     0.000099      0.000095   \n",
       "3     0.013685     0.012807     0.012051     0.010814      0.009851   \n",
       "4     0.002632     0.002379     0.002182     0.001874      0.001653   \n",
       "\n",
       "   cINT1_14400s  \n",
       "0      0.006560  \n",
       "1      0.000450  \n",
       "2      0.000088  \n",
       "3      0.008435  \n",
       "4      0.001348  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw = df[conc_list]\n",
    "x_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_df_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6343/341621659.py:29: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
      "/tmp/ipykernel_6343/341621659.py:30: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
      "/tmp/ipykernel_6343/341621659.py:31: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
      "/tmp/ipykernel_6343/341621659.py:33: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
      "/tmp/ipykernel_6343/341621659.py:34: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
      "/tmp/ipykernel_6343/341621659.py:35: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
      "/tmp/ipykernel_6343/341621659.py:36: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
      "/tmp/ipykernel_6343/341621659.py:38: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
      "/tmp/ipykernel_6343/341621659.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
      "/tmp/ipykernel_6343/341621659.py:40: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
      "/tmp/ipykernel_6343/341621659.py:41: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
      "/tmp/ipykernel_6343/341621659.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
      "/tmp/ipykernel_6343/341621659.py:44: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
      "/tmp/ipykernel_6343/341621659.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
      "/tmp/ipykernel_6343/341621659.py:46: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
      "/tmp/ipykernel_6343/341621659.py:47: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n",
      "/tmp/ipykernel_6343/341621659.py:13: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_0.1s'] = (x_df_raw[conc+'_0.1s']-x_df_raw[conc+'_0s'])/(time_steps[1]-time_steps[0])\n",
      "/tmp/ipykernel_6343/341621659.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1s'] = (x_df_raw[conc+'_1s']-x_df_raw[conc+'_0.1s'])/(time_steps[2]-time_steps[1])\n",
      "/tmp/ipykernel_6343/341621659.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_20s'] = (x_df_raw[conc+'_20s']-x_df_raw[conc+'_1s'])/(time_steps[3]-time_steps[2])\n",
      "/tmp/ipykernel_6343/341621659.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_40s'] = (x_df_raw[conc+'_40s']-x_df_raw[conc+'_20s'])/(time_steps[4]-time_steps[3])\n",
      "/tmp/ipykernel_6343/341621659.py:18: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_60s'] = (x_df_raw[conc+'_60s']-x_df_raw[conc+'_40s'])/(time_steps[5]-time_steps[4])\n",
      "/tmp/ipykernel_6343/341621659.py:19: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_120s'] = (x_df_raw[conc+'_120s']-x_df_raw[conc+'_60s'])/(time_steps[6]-time_steps[5])\n",
      "/tmp/ipykernel_6343/341621659.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_180s'] = (x_df_raw[conc+'_180s']-x_df_raw[conc+'_120s'])/(time_steps[7]-time_steps[6])\n",
      "/tmp/ipykernel_6343/341621659.py:21: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_240s'] = (x_df_raw[conc+'_240s']-x_df_raw[conc+'_180s'])/(time_steps[8]-time_steps[7])\n",
      "/tmp/ipykernel_6343/341621659.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_300s'] = (x_df_raw[conc+'_300s']-x_df_raw[conc+'_240s'])/(time_steps[9]-time_steps[8])\n",
      "/tmp/ipykernel_6343/341621659.py:24: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_360s'] = (x_df_raw[conc+'_360s']-x_df_raw[conc+'_300s'])/(time_steps[10]-time_steps[9])\n",
      "/tmp/ipykernel_6343/341621659.py:25: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_480s'] = (x_df_raw[conc+'_480s']-x_df_raw[conc+'_360s'])/(time_steps[11]-time_steps[10])\n",
      "/tmp/ipykernel_6343/341621659.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_600s'] = (x_df_raw[conc+'_600s']-x_df_raw[conc+'_480s'])/(time_steps[12]-time_steps[11])\n",
      "/tmp/ipykernel_6343/341621659.py:28: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_720s'] = (x_df_raw[conc+'_720s']-x_df_raw[conc+'_600s'])/(time_steps[13]-time_steps[12])\n",
      "/tmp/ipykernel_6343/341621659.py:29: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
      "/tmp/ipykernel_6343/341621659.py:30: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
      "/tmp/ipykernel_6343/341621659.py:31: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
      "/tmp/ipykernel_6343/341621659.py:33: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
      "/tmp/ipykernel_6343/341621659.py:34: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
      "/tmp/ipykernel_6343/341621659.py:35: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
      "/tmp/ipykernel_6343/341621659.py:36: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
      "/tmp/ipykernel_6343/341621659.py:38: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
      "/tmp/ipykernel_6343/341621659.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
      "/tmp/ipykernel_6343/341621659.py:40: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
      "/tmp/ipykernel_6343/341621659.py:41: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
      "/tmp/ipykernel_6343/341621659.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
      "/tmp/ipykernel_6343/341621659.py:44: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
      "/tmp/ipykernel_6343/341621659.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
      "/tmp/ipykernel_6343/341621659.py:46: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
      "/tmp/ipykernel_6343/341621659.py:47: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n",
      "/tmp/ipykernel_6343/341621659.py:13: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_0.1s'] = (x_df_raw[conc+'_0.1s']-x_df_raw[conc+'_0s'])/(time_steps[1]-time_steps[0])\n",
      "/tmp/ipykernel_6343/341621659.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1s'] = (x_df_raw[conc+'_1s']-x_df_raw[conc+'_0.1s'])/(time_steps[2]-time_steps[1])\n",
      "/tmp/ipykernel_6343/341621659.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_20s'] = (x_df_raw[conc+'_20s']-x_df_raw[conc+'_1s'])/(time_steps[3]-time_steps[2])\n",
      "/tmp/ipykernel_6343/341621659.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_40s'] = (x_df_raw[conc+'_40s']-x_df_raw[conc+'_20s'])/(time_steps[4]-time_steps[3])\n",
      "/tmp/ipykernel_6343/341621659.py:18: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_60s'] = (x_df_raw[conc+'_60s']-x_df_raw[conc+'_40s'])/(time_steps[5]-time_steps[4])\n",
      "/tmp/ipykernel_6343/341621659.py:19: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_120s'] = (x_df_raw[conc+'_120s']-x_df_raw[conc+'_60s'])/(time_steps[6]-time_steps[5])\n",
      "/tmp/ipykernel_6343/341621659.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_180s'] = (x_df_raw[conc+'_180s']-x_df_raw[conc+'_120s'])/(time_steps[7]-time_steps[6])\n",
      "/tmp/ipykernel_6343/341621659.py:21: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_240s'] = (x_df_raw[conc+'_240s']-x_df_raw[conc+'_180s'])/(time_steps[8]-time_steps[7])\n",
      "/tmp/ipykernel_6343/341621659.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_300s'] = (x_df_raw[conc+'_300s']-x_df_raw[conc+'_240s'])/(time_steps[9]-time_steps[8])\n",
      "/tmp/ipykernel_6343/341621659.py:24: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_360s'] = (x_df_raw[conc+'_360s']-x_df_raw[conc+'_300s'])/(time_steps[10]-time_steps[9])\n",
      "/tmp/ipykernel_6343/341621659.py:25: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_480s'] = (x_df_raw[conc+'_480s']-x_df_raw[conc+'_360s'])/(time_steps[11]-time_steps[10])\n",
      "/tmp/ipykernel_6343/341621659.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_600s'] = (x_df_raw[conc+'_600s']-x_df_raw[conc+'_480s'])/(time_steps[12]-time_steps[11])\n",
      "/tmp/ipykernel_6343/341621659.py:28: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_720s'] = (x_df_raw[conc+'_720s']-x_df_raw[conc+'_600s'])/(time_steps[13]-time_steps[12])\n",
      "/tmp/ipykernel_6343/341621659.py:29: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
      "/tmp/ipykernel_6343/341621659.py:30: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
      "/tmp/ipykernel_6343/341621659.py:31: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
      "/tmp/ipykernel_6343/341621659.py:33: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
      "/tmp/ipykernel_6343/341621659.py:34: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
      "/tmp/ipykernel_6343/341621659.py:35: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
      "/tmp/ipykernel_6343/341621659.py:36: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
      "/tmp/ipykernel_6343/341621659.py:38: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
      "/tmp/ipykernel_6343/341621659.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
      "/tmp/ipykernel_6343/341621659.py:40: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
      "/tmp/ipykernel_6343/341621659.py:41: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
      "/tmp/ipykernel_6343/341621659.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
      "/tmp/ipykernel_6343/341621659.py:44: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
      "/tmp/ipykernel_6343/341621659.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
      "/tmp/ipykernel_6343/341621659.py:46: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
      "/tmp/ipykernel_6343/341621659.py:47: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n",
      "/tmp/ipykernel_6343/341621659.py:13: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_0.1s'] = (x_df_raw[conc+'_0.1s']-x_df_raw[conc+'_0s'])/(time_steps[1]-time_steps[0])\n",
      "/tmp/ipykernel_6343/341621659.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1s'] = (x_df_raw[conc+'_1s']-x_df_raw[conc+'_0.1s'])/(time_steps[2]-time_steps[1])\n",
      "/tmp/ipykernel_6343/341621659.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_20s'] = (x_df_raw[conc+'_20s']-x_df_raw[conc+'_1s'])/(time_steps[3]-time_steps[2])\n",
      "/tmp/ipykernel_6343/341621659.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_40s'] = (x_df_raw[conc+'_40s']-x_df_raw[conc+'_20s'])/(time_steps[4]-time_steps[3])\n",
      "/tmp/ipykernel_6343/341621659.py:18: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_60s'] = (x_df_raw[conc+'_60s']-x_df_raw[conc+'_40s'])/(time_steps[5]-time_steps[4])\n",
      "/tmp/ipykernel_6343/341621659.py:19: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_120s'] = (x_df_raw[conc+'_120s']-x_df_raw[conc+'_60s'])/(time_steps[6]-time_steps[5])\n",
      "/tmp/ipykernel_6343/341621659.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_180s'] = (x_df_raw[conc+'_180s']-x_df_raw[conc+'_120s'])/(time_steps[7]-time_steps[6])\n",
      "/tmp/ipykernel_6343/341621659.py:21: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_240s'] = (x_df_raw[conc+'_240s']-x_df_raw[conc+'_180s'])/(time_steps[8]-time_steps[7])\n",
      "/tmp/ipykernel_6343/341621659.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_300s'] = (x_df_raw[conc+'_300s']-x_df_raw[conc+'_240s'])/(time_steps[9]-time_steps[8])\n",
      "/tmp/ipykernel_6343/341621659.py:24: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_360s'] = (x_df_raw[conc+'_360s']-x_df_raw[conc+'_300s'])/(time_steps[10]-time_steps[9])\n",
      "/tmp/ipykernel_6343/341621659.py:25: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_480s'] = (x_df_raw[conc+'_480s']-x_df_raw[conc+'_360s'])/(time_steps[11]-time_steps[10])\n",
      "/tmp/ipykernel_6343/341621659.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_600s'] = (x_df_raw[conc+'_600s']-x_df_raw[conc+'_480s'])/(time_steps[12]-time_steps[11])\n",
      "/tmp/ipykernel_6343/341621659.py:28: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_720s'] = (x_df_raw[conc+'_720s']-x_df_raw[conc+'_600s'])/(time_steps[13]-time_steps[12])\n",
      "/tmp/ipykernel_6343/341621659.py:29: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
      "/tmp/ipykernel_6343/341621659.py:30: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
      "/tmp/ipykernel_6343/341621659.py:31: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
      "/tmp/ipykernel_6343/341621659.py:33: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
      "/tmp/ipykernel_6343/341621659.py:34: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
      "/tmp/ipykernel_6343/341621659.py:35: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
      "/tmp/ipykernel_6343/341621659.py:36: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
      "/tmp/ipykernel_6343/341621659.py:38: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
      "/tmp/ipykernel_6343/341621659.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
      "/tmp/ipykernel_6343/341621659.py:40: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
      "/tmp/ipykernel_6343/341621659.py:41: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
      "/tmp/ipykernel_6343/341621659.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
      "/tmp/ipykernel_6343/341621659.py:44: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
      "/tmp/ipykernel_6343/341621659.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
      "/tmp/ipykernel_6343/341621659.py:46: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
      "/tmp/ipykernel_6343/341621659.py:47: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n",
      "/tmp/ipykernel_6343/341621659.py:13: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_0.1s'] = (x_df_raw[conc+'_0.1s']-x_df_raw[conc+'_0s'])/(time_steps[1]-time_steps[0])\n",
      "/tmp/ipykernel_6343/341621659.py:14: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1s'] = (x_df_raw[conc+'_1s']-x_df_raw[conc+'_0.1s'])/(time_steps[2]-time_steps[1])\n",
      "/tmp/ipykernel_6343/341621659.py:15: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_20s'] = (x_df_raw[conc+'_20s']-x_df_raw[conc+'_1s'])/(time_steps[3]-time_steps[2])\n",
      "/tmp/ipykernel_6343/341621659.py:16: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_40s'] = (x_df_raw[conc+'_40s']-x_df_raw[conc+'_20s'])/(time_steps[4]-time_steps[3])\n",
      "/tmp/ipykernel_6343/341621659.py:18: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_60s'] = (x_df_raw[conc+'_60s']-x_df_raw[conc+'_40s'])/(time_steps[5]-time_steps[4])\n",
      "/tmp/ipykernel_6343/341621659.py:19: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_120s'] = (x_df_raw[conc+'_120s']-x_df_raw[conc+'_60s'])/(time_steps[6]-time_steps[5])\n",
      "/tmp/ipykernel_6343/341621659.py:20: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_180s'] = (x_df_raw[conc+'_180s']-x_df_raw[conc+'_120s'])/(time_steps[7]-time_steps[6])\n",
      "/tmp/ipykernel_6343/341621659.py:21: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_240s'] = (x_df_raw[conc+'_240s']-x_df_raw[conc+'_180s'])/(time_steps[8]-time_steps[7])\n",
      "/tmp/ipykernel_6343/341621659.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_300s'] = (x_df_raw[conc+'_300s']-x_df_raw[conc+'_240s'])/(time_steps[9]-time_steps[8])\n",
      "/tmp/ipykernel_6343/341621659.py:24: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_360s'] = (x_df_raw[conc+'_360s']-x_df_raw[conc+'_300s'])/(time_steps[10]-time_steps[9])\n",
      "/tmp/ipykernel_6343/341621659.py:25: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_480s'] = (x_df_raw[conc+'_480s']-x_df_raw[conc+'_360s'])/(time_steps[11]-time_steps[10])\n",
      "/tmp/ipykernel_6343/341621659.py:26: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_600s'] = (x_df_raw[conc+'_600s']-x_df_raw[conc+'_480s'])/(time_steps[12]-time_steps[11])\n",
      "/tmp/ipykernel_6343/341621659.py:28: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_720s'] = (x_df_raw[conc+'_720s']-x_df_raw[conc+'_600s'])/(time_steps[13]-time_steps[12])\n",
      "/tmp/ipykernel_6343/341621659.py:29: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
      "/tmp/ipykernel_6343/341621659.py:30: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
      "/tmp/ipykernel_6343/341621659.py:31: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
      "/tmp/ipykernel_6343/341621659.py:33: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
      "/tmp/ipykernel_6343/341621659.py:34: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
      "/tmp/ipykernel_6343/341621659.py:35: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
      "/tmp/ipykernel_6343/341621659.py:36: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
      "/tmp/ipykernel_6343/341621659.py:38: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
      "/tmp/ipykernel_6343/341621659.py:39: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
      "/tmp/ipykernel_6343/341621659.py:40: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
      "/tmp/ipykernel_6343/341621659.py:41: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
      "/tmp/ipykernel_6343/341621659.py:43: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
      "/tmp/ipykernel_6343/341621659.py:44: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
      "/tmp/ipykernel_6343/341621659.py:45: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
      "/tmp/ipykernel_6343/341621659.py:46: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
      "/tmp/ipykernel_6343/341621659.py:47: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dcSM_dt_0.1s</th>\n",
       "      <th>dcSM_dt_1s</th>\n",
       "      <th>dcSM_dt_20s</th>\n",
       "      <th>dcSM_dt_40s</th>\n",
       "      <th>dcSM_dt_60s</th>\n",
       "      <th>dcSM_dt_120s</th>\n",
       "      <th>dcSM_dt_180s</th>\n",
       "      <th>dcSM_dt_240s</th>\n",
       "      <th>dcSM_dt_300s</th>\n",
       "      <th>dcSM_dt_360s</th>\n",
       "      <th>dcSM_dt_480s</th>\n",
       "      <th>dcSM_dt_600s</th>\n",
       "      <th>dcSM_dt_720s</th>\n",
       "      <th>dcSM_dt_840s</th>\n",
       "      <th>dcSM_dt_960s</th>\n",
       "      <th>dcSM_dt_1080s</th>\n",
       "      <th>dcSM_dt_1200s</th>\n",
       "      <th>dcSM_dt_1500s</th>\n",
       "      <th>dcSM_dt_1800s</th>\n",
       "      <th>dcSM_dt_2400s</th>\n",
       "      <th>dcSM_dt_3000s</th>\n",
       "      <th>dcSM_dt_3600s</th>\n",
       "      <th>dcSM_dt_4500s</th>\n",
       "      <th>dcSM_dt_5400s</th>\n",
       "      <th>dcSM_dt_6300s</th>\n",
       "      <th>dcSM_dt_7200s</th>\n",
       "      <th>dcSM_dt_9000s</th>\n",
       "      <th>dcSM_dt_10800s</th>\n",
       "      <th>dcSM_dt_14400s</th>\n",
       "      <th>dcR_dt_0.1s</th>\n",
       "      <th>dcR_dt_1s</th>\n",
       "      <th>dcR_dt_20s</th>\n",
       "      <th>dcR_dt_40s</th>\n",
       "      <th>dcR_dt_60s</th>\n",
       "      <th>dcR_dt_120s</th>\n",
       "      <th>dcR_dt_180s</th>\n",
       "      <th>dcR_dt_240s</th>\n",
       "      <th>dcR_dt_300s</th>\n",
       "      <th>dcR_dt_360s</th>\n",
       "      <th>dcR_dt_480s</th>\n",
       "      <th>dcR_dt_600s</th>\n",
       "      <th>dcR_dt_720s</th>\n",
       "      <th>dcR_dt_840s</th>\n",
       "      <th>dcR_dt_960s</th>\n",
       "      <th>dcR_dt_1080s</th>\n",
       "      <th>dcR_dt_1200s</th>\n",
       "      <th>dcR_dt_1500s</th>\n",
       "      <th>dcR_dt_1800s</th>\n",
       "      <th>dcR_dt_2400s</th>\n",
       "      <th>dcR_dt_3000s</th>\n",
       "      <th>dcR_dt_3600s</th>\n",
       "      <th>dcR_dt_4500s</th>\n",
       "      <th>dcR_dt_5400s</th>\n",
       "      <th>dcR_dt_6300s</th>\n",
       "      <th>dcR_dt_7200s</th>\n",
       "      <th>dcR_dt_9000s</th>\n",
       "      <th>dcR_dt_10800s</th>\n",
       "      <th>dcR_dt_14400s</th>\n",
       "      <th>dcB_dt_0.1s</th>\n",
       "      <th>dcB_dt_1s</th>\n",
       "      <th>dcB_dt_20s</th>\n",
       "      <th>dcB_dt_40s</th>\n",
       "      <th>dcB_dt_60s</th>\n",
       "      <th>dcB_dt_120s</th>\n",
       "      <th>dcB_dt_180s</th>\n",
       "      <th>dcB_dt_240s</th>\n",
       "      <th>dcB_dt_300s</th>\n",
       "      <th>dcB_dt_360s</th>\n",
       "      <th>dcB_dt_480s</th>\n",
       "      <th>dcB_dt_600s</th>\n",
       "      <th>dcB_dt_720s</th>\n",
       "      <th>dcB_dt_840s</th>\n",
       "      <th>dcB_dt_960s</th>\n",
       "      <th>dcB_dt_1080s</th>\n",
       "      <th>dcB_dt_1200s</th>\n",
       "      <th>dcB_dt_1500s</th>\n",
       "      <th>dcB_dt_1800s</th>\n",
       "      <th>dcB_dt_2400s</th>\n",
       "      <th>dcB_dt_3000s</th>\n",
       "      <th>dcB_dt_3600s</th>\n",
       "      <th>dcB_dt_4500s</th>\n",
       "      <th>dcB_dt_5400s</th>\n",
       "      <th>dcB_dt_6300s</th>\n",
       "      <th>dcB_dt_7200s</th>\n",
       "      <th>dcB_dt_9000s</th>\n",
       "      <th>dcB_dt_10800s</th>\n",
       "      <th>dcB_dt_14400s</th>\n",
       "      <th>dcC_dt_0.1s</th>\n",
       "      <th>dcC_dt_1s</th>\n",
       "      <th>dcC_dt_20s</th>\n",
       "      <th>dcC_dt_40s</th>\n",
       "      <th>dcC_dt_60s</th>\n",
       "      <th>dcC_dt_120s</th>\n",
       "      <th>dcC_dt_180s</th>\n",
       "      <th>dcC_dt_240s</th>\n",
       "      <th>dcC_dt_300s</th>\n",
       "      <th>dcC_dt_360s</th>\n",
       "      <th>dcC_dt_480s</th>\n",
       "      <th>dcC_dt_600s</th>\n",
       "      <th>dcC_dt_720s</th>\n",
       "      <th>dcC_dt_840s</th>\n",
       "      <th>dcC_dt_960s</th>\n",
       "      <th>dcC_dt_1080s</th>\n",
       "      <th>dcC_dt_1200s</th>\n",
       "      <th>dcC_dt_1500s</th>\n",
       "      <th>dcC_dt_1800s</th>\n",
       "      <th>dcC_dt_2400s</th>\n",
       "      <th>dcC_dt_3000s</th>\n",
       "      <th>dcC_dt_3600s</th>\n",
       "      <th>dcC_dt_4500s</th>\n",
       "      <th>dcC_dt_5400s</th>\n",
       "      <th>dcC_dt_6300s</th>\n",
       "      <th>dcC_dt_7200s</th>\n",
       "      <th>dcC_dt_9000s</th>\n",
       "      <th>dcC_dt_10800s</th>\n",
       "      <th>dcC_dt_14400s</th>\n",
       "      <th>dcP_dt_0.1s</th>\n",
       "      <th>dcP_dt_1s</th>\n",
       "      <th>dcP_dt_20s</th>\n",
       "      <th>dcP_dt_40s</th>\n",
       "      <th>dcP_dt_60s</th>\n",
       "      <th>dcP_dt_120s</th>\n",
       "      <th>dcP_dt_180s</th>\n",
       "      <th>dcP_dt_240s</th>\n",
       "      <th>dcP_dt_300s</th>\n",
       "      <th>dcP_dt_360s</th>\n",
       "      <th>dcP_dt_480s</th>\n",
       "      <th>dcP_dt_600s</th>\n",
       "      <th>dcP_dt_720s</th>\n",
       "      <th>dcP_dt_840s</th>\n",
       "      <th>dcP_dt_960s</th>\n",
       "      <th>dcP_dt_1080s</th>\n",
       "      <th>dcP_dt_1200s</th>\n",
       "      <th>dcP_dt_1500s</th>\n",
       "      <th>dcP_dt_1800s</th>\n",
       "      <th>dcP_dt_2400s</th>\n",
       "      <th>dcP_dt_3000s</th>\n",
       "      <th>dcP_dt_3600s</th>\n",
       "      <th>dcP_dt_4500s</th>\n",
       "      <th>dcP_dt_5400s</th>\n",
       "      <th>dcP_dt_6300s</th>\n",
       "      <th>dcP_dt_7200s</th>\n",
       "      <th>dcP_dt_9000s</th>\n",
       "      <th>dcP_dt_10800s</th>\n",
       "      <th>dcP_dt_14400s</th>\n",
       "      <th>dcIMP1_dt_0.1s</th>\n",
       "      <th>dcIMP1_dt_1s</th>\n",
       "      <th>dcIMP1_dt_20s</th>\n",
       "      <th>dcIMP1_dt_40s</th>\n",
       "      <th>dcIMP1_dt_60s</th>\n",
       "      <th>dcIMP1_dt_120s</th>\n",
       "      <th>dcIMP1_dt_180s</th>\n",
       "      <th>dcIMP1_dt_240s</th>\n",
       "      <th>dcIMP1_dt_300s</th>\n",
       "      <th>dcIMP1_dt_360s</th>\n",
       "      <th>dcIMP1_dt_480s</th>\n",
       "      <th>dcIMP1_dt_600s</th>\n",
       "      <th>dcIMP1_dt_720s</th>\n",
       "      <th>dcIMP1_dt_840s</th>\n",
       "      <th>dcIMP1_dt_960s</th>\n",
       "      <th>dcIMP1_dt_1080s</th>\n",
       "      <th>dcIMP1_dt_1200s</th>\n",
       "      <th>dcIMP1_dt_1500s</th>\n",
       "      <th>dcIMP1_dt_1800s</th>\n",
       "      <th>dcIMP1_dt_2400s</th>\n",
       "      <th>dcIMP1_dt_3000s</th>\n",
       "      <th>dcIMP1_dt_3600s</th>\n",
       "      <th>dcIMP1_dt_4500s</th>\n",
       "      <th>dcIMP1_dt_5400s</th>\n",
       "      <th>dcIMP1_dt_6300s</th>\n",
       "      <th>dcIMP1_dt_7200s</th>\n",
       "      <th>dcIMP1_dt_9000s</th>\n",
       "      <th>dcIMP1_dt_10800s</th>\n",
       "      <th>dcIMP1_dt_14400s</th>\n",
       "      <th>dcIMP2_dt_0.1s</th>\n",
       "      <th>dcIMP2_dt_1s</th>\n",
       "      <th>dcIMP2_dt_20s</th>\n",
       "      <th>dcIMP2_dt_40s</th>\n",
       "      <th>dcIMP2_dt_60s</th>\n",
       "      <th>dcIMP2_dt_120s</th>\n",
       "      <th>dcIMP2_dt_180s</th>\n",
       "      <th>dcIMP2_dt_240s</th>\n",
       "      <th>dcIMP2_dt_300s</th>\n",
       "      <th>dcIMP2_dt_360s</th>\n",
       "      <th>dcIMP2_dt_480s</th>\n",
       "      <th>dcIMP2_dt_600s</th>\n",
       "      <th>dcIMP2_dt_720s</th>\n",
       "      <th>dcIMP2_dt_840s</th>\n",
       "      <th>dcIMP2_dt_960s</th>\n",
       "      <th>dcIMP2_dt_1080s</th>\n",
       "      <th>dcIMP2_dt_1200s</th>\n",
       "      <th>dcIMP2_dt_1500s</th>\n",
       "      <th>dcIMP2_dt_1800s</th>\n",
       "      <th>dcIMP2_dt_2400s</th>\n",
       "      <th>dcIMP2_dt_3000s</th>\n",
       "      <th>dcIMP2_dt_3600s</th>\n",
       "      <th>dcIMP2_dt_4500s</th>\n",
       "      <th>dcIMP2_dt_5400s</th>\n",
       "      <th>dcIMP2_dt_6300s</th>\n",
       "      <th>dcIMP2_dt_7200s</th>\n",
       "      <th>dcIMP2_dt_9000s</th>\n",
       "      <th>dcIMP2_dt_10800s</th>\n",
       "      <th>dcIMP2_dt_14400s</th>\n",
       "      <th>dcINT1_dt_0.1s</th>\n",
       "      <th>dcINT1_dt_1s</th>\n",
       "      <th>dcINT1_dt_20s</th>\n",
       "      <th>dcINT1_dt_40s</th>\n",
       "      <th>dcINT1_dt_60s</th>\n",
       "      <th>dcINT1_dt_120s</th>\n",
       "      <th>dcINT1_dt_180s</th>\n",
       "      <th>dcINT1_dt_240s</th>\n",
       "      <th>dcINT1_dt_300s</th>\n",
       "      <th>dcINT1_dt_360s</th>\n",
       "      <th>dcINT1_dt_480s</th>\n",
       "      <th>dcINT1_dt_600s</th>\n",
       "      <th>dcINT1_dt_720s</th>\n",
       "      <th>dcINT1_dt_840s</th>\n",
       "      <th>dcINT1_dt_960s</th>\n",
       "      <th>dcINT1_dt_1080s</th>\n",
       "      <th>dcINT1_dt_1200s</th>\n",
       "      <th>dcINT1_dt_1500s</th>\n",
       "      <th>dcINT1_dt_1800s</th>\n",
       "      <th>dcINT1_dt_2400s</th>\n",
       "      <th>dcINT1_dt_3000s</th>\n",
       "      <th>dcINT1_dt_3600s</th>\n",
       "      <th>dcINT1_dt_4500s</th>\n",
       "      <th>dcINT1_dt_5400s</th>\n",
       "      <th>dcINT1_dt_6300s</th>\n",
       "      <th>dcINT1_dt_7200s</th>\n",
       "      <th>dcINT1_dt_9000s</th>\n",
       "      <th>dcINT1_dt_10800s</th>\n",
       "      <th>dcINT1_dt_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>4.031280e-06</td>\n",
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       "      <td>3.444505e-06</td>\n",
       "      <td>3.345026e-06</td>\n",
       "      <td>3.249535e-06</td>\n",
       "      <td>3.158034e-06</td>\n",
       "      <td>3.070521e-06</td>\n",
       "      <td>2.927846e-06</td>\n",
       "      <td>2.757735e-06</td>\n",
       "      <td>2.525864e-06</td>\n",
       "      <td>2.266000e-06</td>\n",
       "      <td>2.058041e-06</td>\n",
       "      <td>1.838872e-06</td>\n",
       "      <td>1.635747e-06</td>\n",
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       "      <td>9.938329e-07</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>8.070481e-16</td>\n",
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       "      <td>6.978181e-09</td>\n",
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       "      <td>1.219090e-07</td>\n",
       "      <td>1.489589e-07</td>\n",
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       "      <td>2.557053e-07</td>\n",
       "      <td>3.462486e-07</td>\n",
       "      <td>4.605467e-07</td>\n",
       "      <td>5.773542e-07</td>\n",
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       "      <td>7.497610e-07</td>\n",
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       "      <td>4.897452e-07</td>\n",
       "      <td>4.088057e-06</td>\n",
       "      <td>4.087481e-06</td>\n",
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       "      <td>4.053363e-06</td>\n",
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       "      <td>3.982312e-06</td>\n",
       "      <td>3.908883e-06</td>\n",
       "      <td>3.835720e-06</td>\n",
       "      <td>3.762294e-06</td>\n",
       "      <td>3.688606e-06</td>\n",
       "      <td>3.577549e-06</td>\n",
       "      <td>3.425675e-06</td>\n",
       "      <td>3.253623e-06</td>\n",
       "      <td>3.101208e-06</td>\n",
       "      <td>2.951617e-06</td>\n",
       "      <td>2.804852e-06</td>\n",
       "      <td>2.660913e-06</td>\n",
       "      <td>2.416435e-06</td>\n",
       "      <td>2.065238e-06</td>\n",
       "      <td>1.604771e-06</td>\n",
       "      <td>1.111291e-06</td>\n",
       "      <td>7.279296e-07</td>\n",
       "      <td>3.874827e-07</td>\n",
       "      <td>1.459760e-07</td>\n",
       "      <td>-4.181475e-08</td>\n",
       "      <td>-7.684659e-08</td>\n",
       "      <td>-1.669067e-07</td>\n",
       "      <td>-1.769943e-07</td>\n",
       "      <td>-1.699122e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-1.011227e-06</td>\n",
       "      <td>-9.919531e-07</td>\n",
       "      <td>-9.731573e-07</td>\n",
       "      <td>-9.507570e-07</td>\n",
       "      <td>-9.253601e-07</td>\n",
       "      <td>-9.006945e-07</td>\n",
       "      <td>-8.770667e-07</td>\n",
       "      <td>-8.441127e-07</td>\n",
       "      <td>-8.077104e-07</td>\n",
       "      <td>-7.467070e-07</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-1.500387e-06</td>\n",
       "      <td>-1.473848e-06</td>\n",
       "      <td>-1.463975e-06</td>\n",
       "      <td>-1.438870e-06</td>\n",
       "      <td>-1.391806e-06</td>\n",
       "      <td>-1.355885e-06</td>\n",
       "      <td>-1.321234e-06</td>\n",
       "      <td>-1.272299e-06</td>\n",
       "      <td>-1.216270e-06</td>\n",
       "      <td>-1.124400e-06</td>\n",
       "      <td>-5.439052e-07</td>\n",
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       "      <td>-5.563391e-07</td>\n",
       "      <td>-5.622408e-07</td>\n",
       "      <td>-5.687069e-07</td>\n",
       "      <td>-5.795348e-07</td>\n",
       "      <td>-5.979720e-07</td>\n",
       "      <td>-6.314332e-07</td>\n",
       "      <td>-6.505051e-07</td>\n",
       "      <td>-6.678974e-07</td>\n",
       "      <td>-6.836100e-07</td>\n",
       "      <td>-6.976429e-07</td>\n",
       "      <td>-7.144549e-07</td>\n",
       "      <td>-7.342621e-07</td>\n",
       "      <td>-7.501933e-07</td>\n",
       "      <td>-7.369242e-07</td>\n",
       "      <td>-7.319874e-07</td>\n",
       "      <td>-7.194350e-07</td>\n",
       "      <td>-6.959028e-07</td>\n",
       "      <td>-6.779425e-07</td>\n",
       "      <td>-6.606169e-07</td>\n",
       "      <td>-6.361497e-07</td>\n",
       "      <td>-6.081352e-07</td>\n",
       "      <td>-5.622000e-07</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-9.612863e-07</td>\n",
       "      <td>-9.354279e-07</td>\n",
       "      <td>-9.113359e-07</td>\n",
       "      <td>-8.890104e-07</td>\n",
       "      <td>-8.684511e-07</td>\n",
       "      <td>-8.406147e-07</td>\n",
       "      <td>-8.052795e-07</td>\n",
       "      <td>-7.666475e-07</td>\n",
       "      <td>-7.510054e-07</td>\n",
       "      <td>-7.277486e-07</td>\n",
       "      <td>-7.067006e-07</td>\n",
       "      <td>-6.921373e-07</td>\n",
       "      <td>-6.730992e-07</td>\n",
       "      <td>-6.549831e-07</td>\n",
       "      <td>-6.300194e-07</td>\n",
       "      <td>-6.034303e-07</td>\n",
       "      <td>-5.578604e-07</td>\n",
       "      <td>5.439052e-07</td>\n",
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       "      <td>5.396584e-07</td>\n",
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       "      <td>5.331836e-07</td>\n",
       "      <td>5.309065e-07</td>\n",
       "      <td>5.286444e-07</td>\n",
       "      <td>5.264111e-07</td>\n",
       "      <td>5.242068e-07</td>\n",
       "      <td>5.220313e-07</td>\n",
       "      <td>5.183565e-07</td>\n",
       "      <td>5.131805e-07</td>\n",
       "      <td>5.056136e-07</td>\n",
       "      <td>4.959765e-07</td>\n",
       "      <td>4.865787e-07</td>\n",
       "      <td>4.753785e-07</td>\n",
       "      <td>4.626800e-07</td>\n",
       "      <td>4.503472e-07</td>\n",
       "      <td>4.385333e-07</td>\n",
       "      <td>4.220564e-07</td>\n",
       "      <td>4.038552e-07</td>\n",
       "      <td>3.733535e-07</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>9.785989e-16</td>\n",
       "      <td>1.086737e-13</td>\n",
       "      <td>4.527918e-11</td>\n",
       "      <td>3.084362e-10</td>\n",
       "      <td>7.722838e-10</td>\n",
       "      <td>3.357575e-09</td>\n",
       "      <td>1.013860e-08</td>\n",
       "      <td>1.668071e-08</td>\n",
       "      <td>2.377892e-08</td>\n",
       "      <td>3.143323e-08</td>\n",
       "      <td>4.402689e-08</td>\n",
       "      <td>6.478838e-08</td>\n",
       "      <td>1.005267e-07</td>\n",
       "      <td>1.218608e-07</td>\n",
       "      <td>1.414863e-07</td>\n",
       "      <td>1.594032e-07</td>\n",
       "      <td>1.756115e-07</td>\n",
       "      <td>1.960984e-07</td>\n",
       "      <td>2.210816e-07</td>\n",
       "      <td>2.445797e-07</td>\n",
       "      <td>2.409476e-07</td>\n",
       "      <td>2.454087e-07</td>\n",
       "      <td>2.440565e-07</td>\n",
       "      <td>2.332228e-07</td>\n",
       "      <td>2.275953e-07</td>\n",
       "      <td>2.220835e-07</td>\n",
       "      <td>2.140933e-07</td>\n",
       "      <td>2.042801e-07</td>\n",
       "      <td>1.888465e-07</td>\n",
       "      <td>5.439052e-07</td>\n",
       "      <td>5.438948e-07</td>\n",
       "      <td>5.436018e-07</td>\n",
       "      <td>5.426785e-07</td>\n",
       "      <td>5.413444e-07</td>\n",
       "      <td>5.353621e-07</td>\n",
       "      <td>5.205860e-07</td>\n",
       "      <td>5.062970e-07</td>\n",
       "      <td>4.909040e-07</td>\n",
       "      <td>4.744072e-07</td>\n",
       "      <td>4.474542e-07</td>\n",
       "      <td>4.036069e-07</td>\n",
       "      <td>3.298532e-07</td>\n",
       "      <td>2.849228e-07</td>\n",
       "      <td>2.434385e-07</td>\n",
       "      <td>2.054004e-07</td>\n",
       "      <td>1.708083e-07</td>\n",
       "      <td>1.261598e-07</td>\n",
       "      <td>7.101734e-08</td>\n",
       "      <td>1.645423e-08</td>\n",
       "      <td>1.408128e-08</td>\n",
       "      <td>-4.238847e-09</td>\n",
       "      <td>-1.273443e-08</td>\n",
       "      <td>-3.765499e-09</td>\n",
       "      <td>-4.843341e-09</td>\n",
       "      <td>-5.633726e-09</td>\n",
       "      <td>-6.130266e-09</td>\n",
       "      <td>-4.704961e-09</td>\n",
       "      <td>-4.339602e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-2.946227e-06</td>\n",
       "      <td>-2.776611e-06</td>\n",
       "      <td>-2.641983e-06</td>\n",
       "      <td>-2.481567e-06</td>\n",
       "      <td>-2.295799e-06</td>\n",
       "      <td>-2.130274e-06</td>\n",
       "      <td>-1.998729e-06</td>\n",
       "      <td>-1.834316e-06</td>\n",
       "      <td>-1.626607e-06</td>\n",
       "      <td>-1.401146e-06</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-4.423721e-06</td>\n",
       "      <td>-4.169086e-06</td>\n",
       "      <td>-3.966861e-06</td>\n",
       "      <td>-3.725925e-06</td>\n",
       "      <td>-3.446968e-06</td>\n",
       "      <td>-3.198410e-06</td>\n",
       "      <td>-3.000881e-06</td>\n",
       "      <td>-2.754003e-06</td>\n",
       "      <td>-2.442112e-06</td>\n",
       "      <td>-2.103576e-06</td>\n",
       "      <td>-1.841859e-06</td>\n",
       "      <td>-1.841810e-06</td>\n",
       "      <td>-1.872815e-06</td>\n",
       "      <td>-2.041074e-06</td>\n",
       "      <td>-2.198241e-06</td>\n",
       "      <td>-2.439408e-06</td>\n",
       "      <td>-2.641642e-06</td>\n",
       "      <td>-2.660125e-06</td>\n",
       "      <td>-2.693339e-06</td>\n",
       "      <td>-2.680059e-06</td>\n",
       "      <td>-2.630086e-06</td>\n",
       "      <td>-2.598301e-06</td>\n",
       "      <td>-2.566829e-06</td>\n",
       "      <td>-2.535422e-06</td>\n",
       "      <td>-2.502033e-06</td>\n",
       "      <td>-2.470807e-06</td>\n",
       "      <td>-2.440061e-06</td>\n",
       "      <td>-2.387516e-06</td>\n",
       "      <td>-2.314852e-06</td>\n",
       "      <td>-2.211860e-06</td>\n",
       "      <td>-2.084543e-06</td>\n",
       "      <td>-1.983431e-06</td>\n",
       "      <td>-1.862963e-06</td>\n",
       "      <td>-1.723484e-06</td>\n",
       "      <td>-1.599205e-06</td>\n",
       "      <td>-1.500441e-06</td>\n",
       "      <td>-1.377001e-06</td>\n",
       "      <td>-1.221056e-06</td>\n",
       "      <td>-1.051788e-06</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-2.557647e-06</td>\n",
       "      <td>-2.522532e-06</td>\n",
       "      <td>-2.497260e-06</td>\n",
       "      <td>-2.466071e-06</td>\n",
       "      <td>-2.435363e-06</td>\n",
       "      <td>-2.382886e-06</td>\n",
       "      <td>-2.310321e-06</td>\n",
       "      <td>-2.207481e-06</td>\n",
       "      <td>-2.080373e-06</td>\n",
       "      <td>-1.979543e-06</td>\n",
       "      <td>-1.859388e-06</td>\n",
       "      <td>-1.720214e-06</td>\n",
       "      <td>-1.596206e-06</td>\n",
       "      <td>-1.497652e-06</td>\n",
       "      <td>-1.374472e-06</td>\n",
       "      <td>-1.218855e-06</td>\n",
       "      <td>-1.049931e-06</td>\n",
       "      <td>1.841858e-06</td>\n",
       "      <td>1.841741e-06</td>\n",
       "      <td>1.839423e-06</td>\n",
       "      <td>1.834932e-06</td>\n",
       "      <td>1.830404e-06</td>\n",
       "      <td>1.821649e-06</td>\n",
       "      <td>1.808893e-06</td>\n",
       "      <td>1.796485e-06</td>\n",
       "      <td>1.784196e-06</td>\n",
       "      <td>1.772151e-06</td>\n",
       "      <td>1.754402e-06</td>\n",
       "      <td>1.731042e-06</td>\n",
       "      <td>1.708159e-06</td>\n",
       "      <td>1.685985e-06</td>\n",
       "      <td>1.666431e-06</td>\n",
       "      <td>1.645626e-06</td>\n",
       "      <td>1.625141e-06</td>\n",
       "      <td>1.590134e-06</td>\n",
       "      <td>1.541724e-06</td>\n",
       "      <td>1.473114e-06</td>\n",
       "      <td>1.388305e-06</td>\n",
       "      <td>1.320991e-06</td>\n",
       "      <td>1.240783e-06</td>\n",
       "      <td>1.147899e-06</td>\n",
       "      <td>1.065137e-06</td>\n",
       "      <td>9.993644e-07</td>\n",
       "      <td>9.171579e-07</td>\n",
       "      <td>8.133037e-07</td>\n",
       "      <td>7.005731e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.089422e-13</td>\n",
       "      <td>6.905869e-11</td>\n",
       "      <td>3.339178e-08</td>\n",
       "      <td>2.061412e-07</td>\n",
       "      <td>3.678370e-07</td>\n",
       "      <td>6.177591e-07</td>\n",
       "      <td>8.327485e-07</td>\n",
       "      <td>8.636401e-07</td>\n",
       "      <td>9.091432e-07</td>\n",
       "      <td>9.079079e-07</td>\n",
       "      <td>8.756841e-07</td>\n",
       "      <td>8.672594e-07</td>\n",
       "      <td>8.586704e-07</td>\n",
       "      <td>8.494373e-07</td>\n",
       "      <td>8.356024e-07</td>\n",
       "      <td>8.251810e-07</td>\n",
       "      <td>8.149195e-07</td>\n",
       "      <td>7.973819e-07</td>\n",
       "      <td>7.731279e-07</td>\n",
       "      <td>7.387467e-07</td>\n",
       "      <td>6.962378e-07</td>\n",
       "      <td>6.624394e-07</td>\n",
       "      <td>6.221791e-07</td>\n",
       "      <td>5.755844e-07</td>\n",
       "      <td>5.340681e-07</td>\n",
       "      <td>5.010763e-07</td>\n",
       "      <td>4.598434e-07</td>\n",
       "      <td>4.077525e-07</td>\n",
       "      <td>3.512150e-07</td>\n",
       "      <td>1.841857e-06</td>\n",
       "      <td>1.841603e-06</td>\n",
       "      <td>1.772640e-06</td>\n",
       "      <td>1.422650e-06</td>\n",
       "      <td>1.094730e-06</td>\n",
       "      <td>5.861311e-07</td>\n",
       "      <td>1.433965e-07</td>\n",
       "      <td>6.920428e-08</td>\n",
       "      <td>-3.409013e-08</td>\n",
       "      <td>-4.366480e-08</td>\n",
       "      <td>3.033687e-09</td>\n",
       "      <td>-3.476988e-09</td>\n",
       "      <td>-9.181863e-09</td>\n",
       "      <td>-1.288997e-08</td>\n",
       "      <td>-4.773819e-09</td>\n",
       "      <td>-4.735937e-09</td>\n",
       "      <td>-4.697721e-09</td>\n",
       "      <td>-4.629965e-09</td>\n",
       "      <td>-4.531498e-09</td>\n",
       "      <td>-4.379616e-09</td>\n",
       "      <td>-4.170138e-09</td>\n",
       "      <td>-3.887444e-09</td>\n",
       "      <td>-3.574807e-09</td>\n",
       "      <td>-3.269382e-09</td>\n",
       "      <td>-2.999107e-09</td>\n",
       "      <td>-2.788279e-09</td>\n",
       "      <td>-2.528926e-09</td>\n",
       "      <td>-2.201280e-09</td>\n",
       "      <td>-1.856842e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>-0.000102</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>-0.000070</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>-0.000053</td>\n",
       "      <td>-0.000046</td>\n",
       "      <td>-0.000040</td>\n",
       "      <td>-0.000032</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>-0.000024</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>-9.080673e-06</td>\n",
       "      <td>-6.939358e-06</td>\n",
       "      <td>-5.658664e-06</td>\n",
       "      <td>-4.502351e-06</td>\n",
       "      <td>-3.587350e-06</td>\n",
       "      <td>-2.998007e-06</td>\n",
       "      <td>-2.510464e-06</td>\n",
       "      <td>-2.052027e-06</td>\n",
       "      <td>-1.599074e-06</td>\n",
       "      <td>-1.195784e-06</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>-0.000102</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000084</td>\n",
       "      <td>-0.000071</td>\n",
       "      <td>-0.000062</td>\n",
       "      <td>-0.000055</td>\n",
       "      <td>-0.000049</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.000037</td>\n",
       "      <td>-0.000033</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>-0.000027</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>-0.000023</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>-1.471902e-05</td>\n",
       "      <td>-1.184588e-05</td>\n",
       "      <td>-9.897458e-06</td>\n",
       "      <td>-8.069453e-06</td>\n",
       "      <td>-6.517763e-06</td>\n",
       "      <td>-5.472531e-06</td>\n",
       "      <td>-4.604982e-06</td>\n",
       "      <td>-3.765398e-06</td>\n",
       "      <td>-2.933662e-06</td>\n",
       "      <td>-2.187034e-06</td>\n",
       "      <td>-5.681865e-05</td>\n",
       "      <td>-5.670771e-05</td>\n",
       "      <td>-5.461825e-05</td>\n",
       "      <td>-5.095575e-05</td>\n",
       "      <td>-4.752932e-05</td>\n",
       "      <td>-4.198866e-05</td>\n",
       "      <td>-3.561920e-05</td>\n",
       "      <td>-3.088148e-05</td>\n",
       "      <td>-2.746091e-05</td>\n",
       "      <td>-2.442573e-05</td>\n",
       "      <td>-2.162627e-05</td>\n",
       "      <td>-1.848546e-05</td>\n",
       "      <td>-1.638921e-05</td>\n",
       "      <td>-1.467391e-05</td>\n",
       "      <td>-1.348821e-05</td>\n",
       "      <td>-1.233597e-05</td>\n",
       "      <td>-1.148471e-05</td>\n",
       "      <td>-1.025059e-05</td>\n",
       "      <td>-8.869937e-06</td>\n",
       "      <td>-7.359510e-06</td>\n",
       "      <td>-5.922940e-06</td>\n",
       "      <td>-4.948729e-06</td>\n",
       "      <td>-4.034726e-06</td>\n",
       "      <td>-3.258881e-06</td>\n",
       "      <td>-2.736265e-06</td>\n",
       "      <td>-2.302491e-06</td>\n",
       "      <td>-1.882699e-06</td>\n",
       "      <td>-1.466831e-06</td>\n",
       "      <td>-1.093517e-06</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>-0.000102</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>-0.000070</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>-0.000051</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>-2.491907e-05</td>\n",
       "      <td>-2.077705e-05</td>\n",
       "      <td>-1.796922e-05</td>\n",
       "      <td>-1.531546e-05</td>\n",
       "      <td>-1.349336e-05</td>\n",
       "      <td>-1.101066e-05</td>\n",
       "      <td>-8.548837e-06</td>\n",
       "      <td>-6.261499e-06</td>\n",
       "      <td>-4.486096e-06</td>\n",
       "      <td>-3.539266e-06</td>\n",
       "      <td>-2.718801e-06</td>\n",
       "      <td>-2.122144e-06</td>\n",
       "      <td>-1.760745e-06</td>\n",
       "      <td>-1.463205e-06</td>\n",
       "      <td>-1.195342e-06</td>\n",
       "      <td>-9.317805e-07</td>\n",
       "      <td>-7.001592e-07</td>\n",
       "      <td>5.681865e-05</td>\n",
       "      <td>5.670770e-05</td>\n",
       "      <td>5.461164e-05</td>\n",
       "      <td>5.091377e-05</td>\n",
       "      <td>4.743872e-05</td>\n",
       "      <td>4.174738e-05</td>\n",
       "      <td>3.508935e-05</td>\n",
       "      <td>3.003931e-05</td>\n",
       "      <td>2.629489e-05</td>\n",
       "      <td>2.297001e-05</td>\n",
       "      <td>1.976592e-05</td>\n",
       "      <td>1.619324e-05</td>\n",
       "      <td>1.376943e-05</td>\n",
       "      <td>1.181699e-05</td>\n",
       "      <td>1.048581e-05</td>\n",
       "      <td>9.217145e-06</td>\n",
       "      <td>8.326024e-06</td>\n",
       "      <td>7.087083e-06</td>\n",
       "      <td>5.806258e-06</td>\n",
       "      <td>4.540336e-06</td>\n",
       "      <td>3.469679e-06</td>\n",
       "      <td>2.829332e-06</td>\n",
       "      <td>2.251176e-06</td>\n",
       "      <td>1.793675e-06</td>\n",
       "      <td>1.499003e-06</td>\n",
       "      <td>1.255232e-06</td>\n",
       "      <td>1.026014e-06</td>\n",
       "      <td>7.995371e-07</td>\n",
       "      <td>5.978920e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.414843e-13</td>\n",
       "      <td>1.621683e-11</td>\n",
       "      <td>6.615895e-09</td>\n",
       "      <td>4.198782e-08</td>\n",
       "      <td>9.060069e-08</td>\n",
       "      <td>2.412777e-07</td>\n",
       "      <td>5.298501e-07</td>\n",
       "      <td>8.421764e-07</td>\n",
       "      <td>1.166023e-06</td>\n",
       "      <td>1.455722e-06</td>\n",
       "      <td>1.860349e-06</td>\n",
       "      <td>2.292223e-06</td>\n",
       "      <td>2.619782e-06</td>\n",
       "      <td>2.856926e-06</td>\n",
       "      <td>3.002398e-06</td>\n",
       "      <td>3.118826e-06</td>\n",
       "      <td>3.158690e-06</td>\n",
       "      <td>3.163510e-06</td>\n",
       "      <td>3.063679e-06</td>\n",
       "      <td>2.819174e-06</td>\n",
       "      <td>2.453261e-06</td>\n",
       "      <td>2.119397e-06</td>\n",
       "      <td>1.783551e-06</td>\n",
       "      <td>1.465206e-06</td>\n",
       "      <td>1.237262e-06</td>\n",
       "      <td>1.047259e-06</td>\n",
       "      <td>8.566856e-07</td>\n",
       "      <td>6.672937e-07</td>\n",
       "      <td>4.956248e-07</td>\n",
       "      <td>5.681865e-05</td>\n",
       "      <td>5.670766e-05</td>\n",
       "      <td>5.459840e-05</td>\n",
       "      <td>5.082979e-05</td>\n",
       "      <td>4.725752e-05</td>\n",
       "      <td>4.126483e-05</td>\n",
       "      <td>3.402965e-05</td>\n",
       "      <td>2.835495e-05</td>\n",
       "      <td>2.396284e-05</td>\n",
       "      <td>2.005857e-05</td>\n",
       "      <td>1.604522e-05</td>\n",
       "      <td>1.160879e-05</td>\n",
       "      <td>8.529863e-06</td>\n",
       "      <td>6.103138e-06</td>\n",
       "      <td>4.481014e-06</td>\n",
       "      <td>2.979493e-06</td>\n",
       "      <td>2.008643e-06</td>\n",
       "      <td>7.600633e-07</td>\n",
       "      <td>-3.211001e-07</td>\n",
       "      <td>-1.098012e-06</td>\n",
       "      <td>-1.436844e-06</td>\n",
       "      <td>-1.409463e-06</td>\n",
       "      <td>-1.315926e-06</td>\n",
       "      <td>-1.136737e-06</td>\n",
       "      <td>-9.755206e-07</td>\n",
       "      <td>-8.392859e-07</td>\n",
       "      <td>-6.873577e-07</td>\n",
       "      <td>-5.350503e-07</td>\n",
       "      <td>-3.933577e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000588</td>\n",
       "      <td>-0.000582</td>\n",
       "      <td>-0.000488</td>\n",
       "      <td>-0.000360</td>\n",
       "      <td>-0.000280</td>\n",
       "      <td>-0.000195</td>\n",
       "      <td>-0.000129</td>\n",
       "      <td>-0.000095</td>\n",
       "      <td>-0.000075</td>\n",
       "      <td>-0.000062</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>-0.000037</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>-0.000021</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-7.356059e-06</td>\n",
       "      <td>-5.267706e-06</td>\n",
       "      <td>-4.058095e-06</td>\n",
       "      <td>-3.075880e-06</td>\n",
       "      <td>-2.307446e-06</td>\n",
       "      <td>-1.855565e-06</td>\n",
       "      <td>-1.477141e-06</td>\n",
       "      <td>-1.167410e-06</td>\n",
       "      <td>-8.574451e-07</td>\n",
       "      <td>-6.027690e-07</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>-0.000582</td>\n",
       "      <td>-0.000492</td>\n",
       "      <td>-0.000380</td>\n",
       "      <td>-0.000318</td>\n",
       "      <td>-0.000252</td>\n",
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       "    <tr>\n",
       "      <th>1933581</th>\n",
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       "      <td>-1.048326e-06</td>\n",
       "      <td>-7.403697e-07</td>\n",
       "      <td>-5.174351e-07</td>\n",
       "      <td>-3.611708e-07</td>\n",
       "      <td>-2.649095e-07</td>\n",
       "      <td>-2.060375e-07</td>\n",
       "      <td>-1.479290e-07</td>\n",
       "      <td>-1.012615e-07</td>\n",
       "      <td>-6.522632e-08</td>\n",
       "      <td>-2.693084e-02</td>\n",
       "      <td>-1.687926e-02</td>\n",
       "      <td>-2.157870e-03</td>\n",
       "      <td>-3.859420e-04</td>\n",
       "      <td>-1.953459e-04</td>\n",
       "      <td>-9.417556e-05</td>\n",
       "      <td>-4.544848e-05</td>\n",
       "      <td>-2.813605e-05</td>\n",
       "      <td>-1.959604e-05</td>\n",
       "      <td>-1.463252e-05</td>\n",
       "      <td>-1.035748e-05</td>\n",
       "      <td>-7.064342e-06</td>\n",
       "      <td>-5.189360e-06</td>\n",
       "      <td>-3.996113e-06</td>\n",
       "      <td>-3.186346e-06</td>\n",
       "      <td>-2.625765e-06</td>\n",
       "      <td>-2.175694e-06</td>\n",
       "      <td>-1.674383e-06</td>\n",
       "      <td>-1.204291e-06</td>\n",
       "      <td>-8.086435e-07</td>\n",
       "      <td>-5.241631e-07</td>\n",
       "      <td>-3.701848e-07</td>\n",
       "      <td>-2.587175e-07</td>\n",
       "      <td>-1.805854e-07</td>\n",
       "      <td>-1.324547e-07</td>\n",
       "      <td>-1.030188e-07</td>\n",
       "      <td>-7.396451e-08</td>\n",
       "      <td>-5.063075e-08</td>\n",
       "      <td>-3.261316e-08</td>\n",
       "      <td>-0.026931</td>\n",
       "      <td>-0.016879</td>\n",
       "      <td>-0.002158</td>\n",
       "      <td>-0.000386</td>\n",
       "      <td>-0.000195</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>-0.000020</td>\n",
       "      <td>-0.000015</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>-5.189360e-06</td>\n",
       "      <td>-3.996113e-06</td>\n",
       "      <td>-3.186346e-06</td>\n",
       "      <td>-2.625765e-06</td>\n",
       "      <td>-2.175694e-06</td>\n",
       "      <td>-1.674383e-06</td>\n",
       "      <td>-1.204291e-06</td>\n",
       "      <td>-8.086435e-07</td>\n",
       "      <td>-5.241631e-07</td>\n",
       "      <td>-3.701848e-07</td>\n",
       "      <td>-2.587175e-07</td>\n",
       "      <td>-1.805854e-07</td>\n",
       "      <td>-1.324547e-07</td>\n",
       "      <td>-1.030188e-07</td>\n",
       "      <td>-7.396451e-08</td>\n",
       "      <td>-5.063075e-08</td>\n",
       "      <td>-3.261316e-08</td>\n",
       "      <td>5.048965e-06</td>\n",
       "      <td>3.618891e-05</td>\n",
       "      <td>7.893133e-05</td>\n",
       "      <td>6.790235e-05</td>\n",
       "      <td>5.789630e-05</td>\n",
       "      <td>4.620172e-05</td>\n",
       "      <td>3.593379e-05</td>\n",
       "      <td>2.979256e-05</td>\n",
       "      <td>2.559779e-05</td>\n",
       "      <td>2.250896e-05</td>\n",
       "      <td>1.917133e-05</td>\n",
       "      <td>1.598445e-05</td>\n",
       "      <td>1.372577e-05</td>\n",
       "      <td>1.202645e-05</td>\n",
       "      <td>1.069821e-05</td>\n",
       "      <td>9.648936e-06</td>\n",
       "      <td>8.750986e-06</td>\n",
       "      <td>7.573498e-06</td>\n",
       "      <td>6.314217e-06</td>\n",
       "      <td>5.047843e-06</td>\n",
       "      <td>3.943243e-06</td>\n",
       "      <td>3.222267e-06</td>\n",
       "      <td>2.612146e-06</td>\n",
       "      <td>2.112389e-06</td>\n",
       "      <td>1.759699e-06</td>\n",
       "      <td>1.509251e-06</td>\n",
       "      <td>1.236242e-06</td>\n",
       "      <td>9.840276e-07</td>\n",
       "      <td>7.509900e-07</td>\n",
       "      <td>0.026931</td>\n",
       "      <td>0.016879</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.674383e-06</td>\n",
       "      <td>1.204291e-06</td>\n",
       "      <td>8.086435e-07</td>\n",
       "      <td>5.241631e-07</td>\n",
       "      <td>3.701848e-07</td>\n",
       "      <td>2.587175e-07</td>\n",
       "      <td>1.805854e-07</td>\n",
       "      <td>1.324547e-07</td>\n",
       "      <td>1.030188e-07</td>\n",
       "      <td>7.396451e-08</td>\n",
       "      <td>5.063075e-08</td>\n",
       "      <td>3.261316e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.692579e-02</td>\n",
       "      <td>1.684307e-02</td>\n",
       "      <td>2.078939e-03</td>\n",
       "      <td>3.180396e-04</td>\n",
       "      <td>1.374495e-04</td>\n",
       "      <td>4.797383e-05</td>\n",
       "      <td>9.514680e-06</td>\n",
       "      <td>-1.656507e-06</td>\n",
       "      <td>-6.001757e-06</td>\n",
       "      <td>-7.876447e-06</td>\n",
       "      <td>-8.813852e-06</td>\n",
       "      <td>-8.920112e-06</td>\n",
       "      <td>-8.536409e-06</td>\n",
       "      <td>-8.030337e-06</td>\n",
       "      <td>-7.511859e-06</td>\n",
       "      <td>-7.023172e-06</td>\n",
       "      <td>-6.575293e-06</td>\n",
       "      <td>-5.899115e-06</td>\n",
       "      <td>-5.109926e-06</td>\n",
       "      <td>-4.239199e-06</td>\n",
       "      <td>-3.419080e-06</td>\n",
       "      <td>-2.852082e-06</td>\n",
       "      <td>-2.353428e-06</td>\n",
       "      <td>-1.931804e-06</td>\n",
       "      <td>-1.627244e-06</td>\n",
       "      <td>-1.406232e-06</td>\n",
       "      <td>-1.162278e-06</td>\n",
       "      <td>-9.333969e-07</td>\n",
       "      <td>-7.183769e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>-0.119941</td>\n",
       "      <td>-0.052218</td>\n",
       "      <td>-0.004104</td>\n",
       "      <td>-0.000605</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000026</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-8.423171e-07</td>\n",
       "      <td>-5.287683e-07</td>\n",
       "      <td>-3.735088e-07</td>\n",
       "      <td>-2.494098e-07</td>\n",
       "      <td>-1.750814e-07</td>\n",
       "      <td>-1.230243e-07</td>\n",
       "      <td>-1.008656e-07</td>\n",
       "      <td>-6.518779e-08</td>\n",
       "      <td>-5.497757e-08</td>\n",
       "      <td>-2.469737e-08</td>\n",
       "      <td>-0.119941</td>\n",
       "      <td>-0.052218</td>\n",
       "      <td>-0.004104</td>\n",
       "      <td>-0.000605</td>\n",
       "      <td>-0.000295</td>\n",
       "      <td>-0.000136</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>-0.000026</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-0.000003</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000002</td>\n",
       "      <td>-0.000001</td>\n",
       "      <td>-8.423171e-07</td>\n",
       "      <td>-5.287683e-07</td>\n",
       "      <td>-3.735088e-07</td>\n",
       "      <td>-2.494098e-07</td>\n",
       "      <td>-1.750814e-07</td>\n",
       "      <td>-1.230243e-07</td>\n",
       "      <td>-1.008656e-07</td>\n",
       "      <td>-6.518779e-08</td>\n",
       "      <td>-5.497757e-08</td>\n",
       "      <td>-2.469737e-08</td>\n",
       "      <td>-5.997070e-02</td>\n",
       "      <td>-2.610881e-02</td>\n",
       "      <td>-2.051835e-03</td>\n",
       "      <td>-3.026561e-04</td>\n",
       "      <td>-1.475444e-04</td>\n",
       "      <td>-6.824514e-05</td>\n",
       "      <td>-3.143453e-05</td>\n",
       "      <td>-1.886508e-05</td>\n",
       "      <td>-1.279510e-05</td>\n",
       "      <td>-9.354741e-06</td>\n",
       "      <td>-6.440185e-06</td>\n",
       "      <td>-4.279103e-06</td>\n",
       "      <td>-3.076004e-06</td>\n",
       "      <td>-2.315004e-06</td>\n",
       "      <td>-1.824643e-06</td>\n",
       "      <td>-1.470473e-06</td>\n",
       "      <td>-1.220686e-06</td>\n",
       "      <td>-9.141715e-07</td>\n",
       "      <td>-6.365700e-07</td>\n",
       "      <td>-4.211586e-07</td>\n",
       "      <td>-2.643841e-07</td>\n",
       "      <td>-1.867544e-07</td>\n",
       "      <td>-1.247049e-07</td>\n",
       "      <td>-8.754069e-08</td>\n",
       "      <td>-6.151214e-08</td>\n",
       "      <td>-5.043279e-08</td>\n",
       "      <td>-3.259390e-08</td>\n",
       "      <td>-2.748878e-08</td>\n",
       "      <td>-1.234869e-08</td>\n",
       "      <td>-0.059971</td>\n",
       "      <td>-0.026109</td>\n",
       "      <td>-0.002052</td>\n",
       "      <td>-0.000303</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>-0.000019</td>\n",
       "      <td>-0.000013</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000006</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-3.076004e-06</td>\n",
       "      <td>-2.315004e-06</td>\n",
       "      <td>-1.824643e-06</td>\n",
       "      <td>-1.470473e-06</td>\n",
       "      <td>-1.220686e-06</td>\n",
       "      <td>-9.141715e-07</td>\n",
       "      <td>-6.365700e-07</td>\n",
       "      <td>-4.211586e-07</td>\n",
       "      <td>-2.643841e-07</td>\n",
       "      <td>-1.867544e-07</td>\n",
       "      <td>-1.247049e-07</td>\n",
       "      <td>-8.754069e-08</td>\n",
       "      <td>-6.151214e-08</td>\n",
       "      <td>-5.043279e-08</td>\n",
       "      <td>-3.259390e-08</td>\n",
       "      <td>-2.748878e-08</td>\n",
       "      <td>-1.234869e-08</td>\n",
       "      <td>2.341640e-03</td>\n",
       "      <td>9.737870e-03</td>\n",
       "      <td>2.945537e-03</td>\n",
       "      <td>3.926387e-04</td>\n",
       "      <td>1.749456e-04</td>\n",
       "      <td>7.728380e-05</td>\n",
       "      <td>3.456667e-05</td>\n",
       "      <td>2.049380e-05</td>\n",
       "      <td>1.380191e-05</td>\n",
       "      <td>1.004454e-05</td>\n",
       "      <td>6.884557e-06</td>\n",
       "      <td>4.555979e-06</td>\n",
       "      <td>3.266368e-06</td>\n",
       "      <td>2.453337e-06</td>\n",
       "      <td>1.931095e-06</td>\n",
       "      <td>1.554397e-06</td>\n",
       "      <td>1.289325e-06</td>\n",
       "      <td>9.643738e-07</td>\n",
       "      <td>6.706301e-07</td>\n",
       "      <td>4.431959e-07</td>\n",
       "      <td>2.779091e-07</td>\n",
       "      <td>1.961900e-07</td>\n",
       "      <td>1.309093e-07</td>\n",
       "      <td>9.186239e-08</td>\n",
       "      <td>6.451554e-08</td>\n",
       "      <td>5.289476e-08</td>\n",
       "      <td>3.416669e-08</td>\n",
       "      <td>2.881831e-08</td>\n",
       "      <td>1.293586e-08</td>\n",
       "      <td>0.059971</td>\n",
       "      <td>0.026109</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.141715e-07</td>\n",
       "      <td>6.365700e-07</td>\n",
       "      <td>4.211586e-07</td>\n",
       "      <td>2.643841e-07</td>\n",
       "      <td>1.867544e-07</td>\n",
       "      <td>1.247049e-07</td>\n",
       "      <td>8.754069e-08</td>\n",
       "      <td>6.151214e-08</td>\n",
       "      <td>5.043279e-08</td>\n",
       "      <td>3.259390e-08</td>\n",
       "      <td>2.748878e-08</td>\n",
       "      <td>1.234869e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.762906e-02</td>\n",
       "      <td>1.637094e-02</td>\n",
       "      <td>-8.937028e-04</td>\n",
       "      <td>-8.998255e-05</td>\n",
       "      <td>-2.740118e-05</td>\n",
       "      <td>-9.038659e-06</td>\n",
       "      <td>-3.132139e-06</td>\n",
       "      <td>-1.628720e-06</td>\n",
       "      <td>-1.006818e-06</td>\n",
       "      <td>-6.897982e-07</td>\n",
       "      <td>-4.443722e-07</td>\n",
       "      <td>-2.768760e-07</td>\n",
       "      <td>-1.903640e-07</td>\n",
       "      <td>-1.383322e-07</td>\n",
       "      <td>-1.064525e-07</td>\n",
       "      <td>-8.392418e-08</td>\n",
       "      <td>-6.863888e-08</td>\n",
       "      <td>-5.020226e-08</td>\n",
       "      <td>-3.406008e-08</td>\n",
       "      <td>-2.203734e-08</td>\n",
       "      <td>-1.352491e-08</td>\n",
       "      <td>-9.435631e-09</td>\n",
       "      <td>-6.204366e-09</td>\n",
       "      <td>-4.321705e-09</td>\n",
       "      <td>-3.003400e-09</td>\n",
       "      <td>-2.461975e-09</td>\n",
       "      <td>-1.572789e-09</td>\n",
       "      <td>-1.329523e-09</td>\n",
       "      <td>-5.871740e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>-0.088900</td>\n",
       "      <td>-0.045184</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.606052e-06</td>\n",
       "      <td>1.605992e-06</td>\n",
       "      <td>1.604791e-06</td>\n",
       "      <td>1.602428e-06</td>\n",
       "      <td>1.599990e-06</td>\n",
       "      <td>1.595042e-06</td>\n",
       "      <td>1.587482e-06</td>\n",
       "      <td>1.579836e-06</td>\n",
       "      <td>1.572088e-06</td>\n",
       "      <td>1.564235e-06</td>\n",
       "      <td>1.552249e-06</td>\n",
       "      <td>1.535677e-06</td>\n",
       "      <td>1.516950e-06</td>\n",
       "      <td>1.499360e-06</td>\n",
       "      <td>1.481550e-06</td>\n",
       "      <td>1.463521e-06</td>\n",
       "      <td>1.445273e-06</td>\n",
       "      <td>1.412763e-06</td>\n",
       "      <td>1.365223e-06</td>\n",
       "      <td>1.289656e-06</td>\n",
       "      <td>1.181904e-06</td>\n",
       "      <td>1.080378e-06</td>\n",
       "      <td>9.504362e-07</td>\n",
       "      <td>8.034834e-07</td>\n",
       "      <td>6.689125e-07</td>\n",
       "      <td>5.478341e-07</td>\n",
       "      <td>3.978180e-07</td>\n",
       "      <td>2.416882e-07</td>\n",
       "      <td>9.574263e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000007</td>\n",
       "      <td>-6.650994e-06</td>\n",
       "      <td>-6.040530e-06</td>\n",
       "      <td>-5.525338e-06</td>\n",
       "      <td>-4.988898e-06</td>\n",
       "      <td>-4.451762e-06</td>\n",
       "      <td>-4.016025e-06</td>\n",
       "      <td>-3.649468e-06</td>\n",
       "      <td>-3.207485e-06</td>\n",
       "      <td>-2.745368e-06</td>\n",
       "      <td>-2.252996e-06</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>-0.000009</td>\n",
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       "      <td>-0.000008</td>\n",
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       "      <td>-6.650994e-06</td>\n",
       "      <td>-6.040530e-06</td>\n",
       "      <td>-5.525338e-06</td>\n",
       "      <td>-4.988898e-06</td>\n",
       "      <td>-4.451762e-06</td>\n",
       "      <td>-4.016025e-06</td>\n",
       "      <td>-3.649468e-06</td>\n",
       "      <td>-3.207485e-06</td>\n",
       "      <td>-2.745368e-06</td>\n",
       "      <td>-2.252996e-06</td>\n",
       "      <td>-5.021573e-06</td>\n",
       "      <td>-5.020985e-06</td>\n",
       "      <td>-5.009312e-06</td>\n",
       "      <td>-4.986570e-06</td>\n",
       "      <td>-4.963431e-06</td>\n",
       "      <td>-4.917835e-06</td>\n",
       "      <td>-4.850798e-06</td>\n",
       "      <td>-4.785260e-06</td>\n",
       "      <td>-4.721272e-06</td>\n",
       "      <td>-4.658962e-06</td>\n",
       "      <td>-4.568608e-06</td>\n",
       "      <td>-4.452267e-06</td>\n",
       "      <td>-4.341797e-06</td>\n",
       "      <td>-4.235624e-06</td>\n",
       "      <td>-4.134706e-06</td>\n",
       "      <td>-4.037617e-06</td>\n",
       "      <td>-3.944617e-06</td>\n",
       "      <td>-3.792182e-06</td>\n",
       "      <td>-3.591225e-06</td>\n",
       "      <td>-3.325497e-06</td>\n",
       "      <td>-3.020265e-06</td>\n",
       "      <td>-2.762669e-06</td>\n",
       "      <td>-2.494449e-06</td>\n",
       "      <td>-2.225881e-06</td>\n",
       "      <td>-2.008012e-06</td>\n",
       "      <td>-1.824734e-06</td>\n",
       "      <td>-1.603742e-06</td>\n",
       "      <td>-1.372684e-06</td>\n",
       "      <td>-1.126498e-06</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
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       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>-0.000004</td>\n",
       "      <td>-4.341797e-06</td>\n",
       "      <td>-4.235624e-06</td>\n",
       "      <td>-4.134706e-06</td>\n",
       "      <td>-4.037617e-06</td>\n",
       "      <td>-3.944617e-06</td>\n",
       "      <td>-3.792182e-06</td>\n",
       "      <td>-3.591225e-06</td>\n",
       "      <td>-3.325497e-06</td>\n",
       "      <td>-3.020265e-06</td>\n",
       "      <td>-2.762669e-06</td>\n",
       "      <td>-2.494449e-06</td>\n",
       "      <td>-2.225881e-06</td>\n",
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       "      <td>-1.824734e-06</td>\n",
       "      <td>-1.603742e-06</td>\n",
       "      <td>-1.372684e-06</td>\n",
       "      <td>-1.126498e-06</td>\n",
       "      <td>1.110223e-14</td>\n",
       "      <td>1.232872e-12</td>\n",
       "      <td>5.031335e-10</td>\n",
       "      <td>3.389474e-09</td>\n",
       "      <td>8.541778e-09</td>\n",
       "      <td>2.921315e-08</td>\n",
       "      <td>7.787188e-08</td>\n",
       "      <td>1.363369e-07</td>\n",
       "      <td>2.072708e-07</td>\n",
       "      <td>2.980277e-07</td>\n",
       "      <td>4.597933e-07</td>\n",
       "      <td>6.570668e-07</td>\n",
       "      <td>8.787266e-07</td>\n",
       "      <td>1.062099e-06</td>\n",
       "      <td>1.228172e-06</td>\n",
       "      <td>1.366187e-06</td>\n",
       "      <td>1.474165e-06</td>\n",
       "      <td>1.596304e-06</td>\n",
       "      <td>1.677470e-06</td>\n",
       "      <td>1.676404e-06</td>\n",
       "      <td>1.586956e-06</td>\n",
       "      <td>1.467840e-06</td>\n",
       "      <td>1.331547e-06</td>\n",
       "      <td>1.188640e-06</td>\n",
       "      <td>1.070695e-06</td>\n",
       "      <td>9.719106e-07</td>\n",
       "      <td>8.527131e-07</td>\n",
       "      <td>7.283900e-07</td>\n",
       "      <td>5.963261e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>3.792182e-06</td>\n",
       "      <td>3.591225e-06</td>\n",
       "      <td>3.325497e-06</td>\n",
       "      <td>3.020265e-06</td>\n",
       "      <td>2.762669e-06</td>\n",
       "      <td>2.494449e-06</td>\n",
       "      <td>2.225881e-06</td>\n",
       "      <td>2.008012e-06</td>\n",
       "      <td>1.824734e-06</td>\n",
       "      <td>1.603742e-06</td>\n",
       "      <td>1.372684e-06</td>\n",
       "      <td>1.126498e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.021573e-06</td>\n",
       "      <td>5.020982e-06</td>\n",
       "      <td>5.008306e-06</td>\n",
       "      <td>4.979791e-06</td>\n",
       "      <td>4.946347e-06</td>\n",
       "      <td>4.859409e-06</td>\n",
       "      <td>4.695054e-06</td>\n",
       "      <td>4.512587e-06</td>\n",
       "      <td>4.306731e-06</td>\n",
       "      <td>4.062907e-06</td>\n",
       "      <td>3.649021e-06</td>\n",
       "      <td>3.138133e-06</td>\n",
       "      <td>2.584344e-06</td>\n",
       "      <td>2.111426e-06</td>\n",
       "      <td>1.678362e-06</td>\n",
       "      <td>1.305243e-06</td>\n",
       "      <td>9.962871e-07</td>\n",
       "      <td>5.995745e-07</td>\n",
       "      <td>2.362847e-07</td>\n",
       "      <td>-2.731091e-08</td>\n",
       "      <td>-1.536468e-07</td>\n",
       "      <td>-1.730115e-07</td>\n",
       "      <td>-1.686449e-07</td>\n",
       "      <td>-1.513983e-07</td>\n",
       "      <td>-1.333769e-07</td>\n",
       "      <td>-1.190871e-07</td>\n",
       "      <td>-1.016838e-07</td>\n",
       "      <td>-8.409586e-08</td>\n",
       "      <td>-6.615415e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1933586 rows × 232 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         dcSM_dt_0.1s  dcSM_dt_1s  dcSM_dt_20s  dcSM_dt_40s  dcSM_dt_60s  \\\n",
       "0           -0.000008   -0.000008    -0.000008    -0.000008    -0.000008   \n",
       "1           -0.000001   -0.000001    -0.000001    -0.000001    -0.000001   \n",
       "2           -0.000004   -0.000004    -0.000004    -0.000004    -0.000004   \n",
       "3           -0.000114   -0.000113    -0.000109    -0.000102    -0.000095   \n",
       "4           -0.000588   -0.000582    -0.000488    -0.000360    -0.000280   \n",
       "...               ...         ...          ...          ...          ...   \n",
       "1933581     -0.053862   -0.033759    -0.004316    -0.000772    -0.000391   \n",
       "1933582     -0.119941   -0.052218    -0.004104    -0.000605    -0.000295   \n",
       "1933583     -0.088900   -0.045184    -0.004239    -0.000669    -0.000331   \n",
       "1933584     -0.000003   -0.000003    -0.000003    -0.000003    -0.000003   \n",
       "1933585     -0.000010   -0.000010    -0.000010    -0.000010    -0.000010   \n",
       "\n",
       "         dcSM_dt_120s  dcSM_dt_180s  dcSM_dt_240s  dcSM_dt_300s  dcSM_dt_360s  \\\n",
       "0           -0.000008     -0.000008     -0.000008     -0.000008     -0.000007   \n",
       "1           -0.000001     -0.000001     -0.000001     -0.000001     -0.000001   \n",
       "2           -0.000004     -0.000004     -0.000004     -0.000004     -0.000004   \n",
       "3           -0.000083     -0.000070     -0.000060     -0.000053     -0.000046   \n",
       "4           -0.000195     -0.000129     -0.000095     -0.000075     -0.000062   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581     -0.000188     -0.000091     -0.000056     -0.000039     -0.000029   \n",
       "1933582     -0.000136     -0.000063     -0.000038     -0.000026     -0.000019   \n",
       "1933583     -0.000156     -0.000073     -0.000044     -0.000030     -0.000022   \n",
       "1933584     -0.000003     -0.000003     -0.000003     -0.000003     -0.000003   \n",
       "1933585     -0.000010     -0.000010     -0.000010     -0.000009     -0.000009   \n",
       "\n",
       "         dcSM_dt_480s  dcSM_dt_600s  dcSM_dt_720s  dcSM_dt_840s  dcSM_dt_960s  \\\n",
       "0           -0.000007     -0.000007     -0.000007     -0.000007     -0.000006   \n",
       "1           -0.000001     -0.000001     -0.000001     -0.000001     -0.000001   \n",
       "2           -0.000004     -0.000003     -0.000003     -0.000003     -0.000003   \n",
       "3           -0.000040     -0.000032     -0.000028     -0.000024     -0.000021   \n",
       "4           -0.000048     -0.000037     -0.000029     -0.000025     -0.000021   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581     -0.000021     -0.000014     -0.000010     -0.000008     -0.000006   \n",
       "1933582     -0.000013     -0.000009     -0.000006     -0.000005     -0.000004   \n",
       "1933583     -0.000016     -0.000010     -0.000008     -0.000006     -0.000005   \n",
       "1933584     -0.000003     -0.000003     -0.000003     -0.000003     -0.000003   \n",
       "1933585     -0.000009     -0.000009     -0.000009     -0.000008     -0.000008   \n",
       "\n",
       "         dcSM_dt_1080s  dcSM_dt_1200s  dcSM_dt_1500s  dcSM_dt_1800s  \\\n",
       "0            -0.000006      -0.000006      -0.000006      -0.000006   \n",
       "1            -0.000001      -0.000001      -0.000001      -0.000001   \n",
       "2            -0.000003      -0.000003      -0.000003      -0.000003   \n",
       "3            -0.000018      -0.000017      -0.000014      -0.000012   \n",
       "4            -0.000018      -0.000015      -0.000013      -0.000010   \n",
       "...                ...            ...            ...            ...   \n",
       "1933581      -0.000005      -0.000004      -0.000003      -0.000002   \n",
       "1933582      -0.000003      -0.000002      -0.000002      -0.000001   \n",
       "1933583      -0.000004      -0.000003      -0.000002      -0.000002   \n",
       "1933584      -0.000003      -0.000003      -0.000003      -0.000003   \n",
       "1933585      -0.000008      -0.000008      -0.000008      -0.000007   \n",
       "\n",
       "         dcSM_dt_2400s  dcSM_dt_3000s  dcSM_dt_3600s  dcSM_dt_4500s  \\\n",
       "0        -5.051729e-06  -4.531999e-06  -4.116083e-06  -3.677744e-06   \n",
       "1        -1.011227e-06  -9.919531e-07  -9.731573e-07  -9.507570e-07   \n",
       "2        -2.946227e-06  -2.776611e-06  -2.641983e-06  -2.481567e-06   \n",
       "3        -9.080673e-06  -6.939358e-06  -5.658664e-06  -4.502351e-06   \n",
       "4        -7.356059e-06  -5.267706e-06  -4.058095e-06  -3.075880e-06   \n",
       "...                ...            ...            ...            ...   \n",
       "1933581  -1.617287e-06  -1.048326e-06  -7.403697e-07  -5.174351e-07   \n",
       "1933582  -8.423171e-07  -5.287683e-07  -3.735088e-07  -2.494098e-07   \n",
       "1933583  -1.091450e-06  -7.017644e-07  -4.857558e-07  -3.320042e-07   \n",
       "1933584  -2.770258e-06  -2.664968e-06  -2.564681e-06  -2.450600e-06   \n",
       "1933585  -6.650994e-06  -6.040530e-06  -5.525338e-06  -4.988898e-06   \n",
       "\n",
       "         dcSM_dt_5400s  dcSM_dt_6300s  dcSM_dt_7200s  dcSM_dt_9000s  \\\n",
       "0        -3.271494e-06  -2.915415e-06  -2.669564e-06  -2.317520e-06   \n",
       "1        -9.253601e-07  -9.006945e-07  -8.770667e-07  -8.441127e-07   \n",
       "2        -2.295799e-06  -2.130274e-06  -1.998729e-06  -1.834316e-06   \n",
       "3        -3.587350e-06  -2.998007e-06  -2.510464e-06  -2.052027e-06   \n",
       "4        -2.307446e-06  -1.855565e-06  -1.477141e-06  -1.167410e-06   \n",
       "...                ...            ...            ...            ...   \n",
       "1933581  -3.611708e-07  -2.649095e-07  -2.060375e-07  -1.479290e-07   \n",
       "1933582  -1.750814e-07  -1.230243e-07  -1.008656e-07  -6.518779e-08   \n",
       "1933583  -2.342006e-07  -1.665874e-07  -1.331211e-07  -9.022943e-08   \n",
       "1933584  -2.324323e-06  -2.209943e-06  -2.105090e-06  -1.964970e-06   \n",
       "1933585  -4.451762e-06  -4.016025e-06  -3.649468e-06  -3.207485e-06   \n",
       "\n",
       "         dcSM_dt_10800s  dcSM_dt_14400s  dcR_dt_0.1s  dcR_dt_1s  dcR_dt_20s  \\\n",
       "0         -1.987666e-06   -1.619157e-06    -0.000008  -0.000008   -0.000008   \n",
       "1         -8.077104e-07   -7.467070e-07    -0.000001  -0.000001   -0.000001   \n",
       "2         -1.626607e-06   -1.401146e-06    -0.000004  -0.000004   -0.000004   \n",
       "3         -1.599074e-06   -1.195784e-06    -0.000114  -0.000113   -0.000109   \n",
       "4         -8.574451e-07   -6.027690e-07    -0.000588  -0.000582   -0.000492   \n",
       "...                 ...             ...          ...        ...         ...   \n",
       "1933581   -1.012615e-07   -6.522632e-08    -0.053862  -0.033759   -0.004316   \n",
       "1933582   -5.497757e-08   -2.469737e-08    -0.119941  -0.052218   -0.004104   \n",
       "1933583   -6.402461e-08   -3.916186e-08    -0.088900  -0.045184   -0.004239   \n",
       "1933584   -1.801081e-06   -1.600227e-06    -0.000003  -0.000003   -0.000003   \n",
       "1933585   -2.745368e-06   -2.252996e-06    -0.000010  -0.000010   -0.000010   \n",
       "\n",
       "         dcR_dt_40s  dcR_dt_60s  dcR_dt_120s  dcR_dt_180s  dcR_dt_240s  \\\n",
       "0         -0.000008   -0.000008    -0.000008    -0.000008    -0.000008   \n",
       "1         -0.000001   -0.000001    -0.000001    -0.000001    -0.000001   \n",
       "2         -0.000004   -0.000004    -0.000005    -0.000005    -0.000005   \n",
       "3         -0.000102   -0.000095    -0.000084    -0.000071    -0.000062   \n",
       "4         -0.000380   -0.000318    -0.000252    -0.000191    -0.000152   \n",
       "...             ...         ...          ...          ...          ...   \n",
       "1933581   -0.000772   -0.000391    -0.000188    -0.000091    -0.000056   \n",
       "1933582   -0.000605   -0.000295    -0.000136    -0.000063    -0.000038   \n",
       "1933583   -0.000669   -0.000331    -0.000156    -0.000073    -0.000044   \n",
       "1933584   -0.000003   -0.000003    -0.000003    -0.000003    -0.000003   \n",
       "1933585   -0.000010   -0.000010    -0.000010    -0.000010    -0.000010   \n",
       "\n",
       "         dcR_dt_300s  dcR_dt_360s  dcR_dt_480s  dcR_dt_600s  dcR_dt_720s  \\\n",
       "0          -0.000008    -0.000008    -0.000007    -0.000007    -0.000007   \n",
       "1          -0.000001    -0.000001    -0.000001    -0.000001    -0.000001   \n",
       "2          -0.000005    -0.000005    -0.000005    -0.000005    -0.000005   \n",
       "3          -0.000055    -0.000049    -0.000043    -0.000037    -0.000033   \n",
       "4          -0.000124    -0.000103    -0.000082    -0.000063    -0.000050   \n",
       "...              ...          ...          ...          ...          ...   \n",
       "1933581    -0.000039    -0.000029    -0.000021    -0.000014    -0.000010   \n",
       "1933582    -0.000026    -0.000019    -0.000013    -0.000009    -0.000006   \n",
       "1933583    -0.000030    -0.000022    -0.000016    -0.000010    -0.000008   \n",
       "1933584    -0.000003    -0.000003    -0.000003    -0.000003    -0.000003   \n",
       "1933585    -0.000009    -0.000009    -0.000009    -0.000009    -0.000009   \n",
       "\n",
       "         dcR_dt_840s  dcR_dt_960s  dcR_dt_1080s  dcR_dt_1200s  dcR_dt_1500s  \\\n",
       "0          -0.000007    -0.000007     -0.000007     -0.000007     -0.000006   \n",
       "1          -0.000001    -0.000001     -0.000001     -0.000001     -0.000001   \n",
       "2          -0.000005    -0.000005     -0.000005     -0.000005     -0.000005   \n",
       "3          -0.000029    -0.000027     -0.000025     -0.000023     -0.000021   \n",
       "4          -0.000042    -0.000035     -0.000030     -0.000026     -0.000022   \n",
       "...              ...          ...           ...           ...           ...   \n",
       "1933581    -0.000008    -0.000006     -0.000005     -0.000004     -0.000003   \n",
       "1933582    -0.000005    -0.000004     -0.000003     -0.000002     -0.000002   \n",
       "1933583    -0.000006    -0.000005     -0.000004     -0.000003     -0.000002   \n",
       "1933584    -0.000003    -0.000003     -0.000003     -0.000003     -0.000003   \n",
       "1933585    -0.000008    -0.000008     -0.000008     -0.000008     -0.000008   \n",
       "\n",
       "         dcR_dt_1800s  dcR_dt_2400s  dcR_dt_3000s  dcR_dt_3600s  dcR_dt_4500s  \\\n",
       "0           -0.000006 -5.972822e-06 -5.686708e-06 -5.446195e-06 -5.129133e-06   \n",
       "1           -0.000001 -1.500387e-06 -1.473848e-06 -1.463975e-06 -1.438870e-06   \n",
       "2           -0.000005 -4.423721e-06 -4.169086e-06 -3.966861e-06 -3.725925e-06   \n",
       "3           -0.000018 -1.471902e-05 -1.184588e-05 -9.897458e-06 -8.069453e-06   \n",
       "4           -0.000017 -1.230240e-05 -8.777810e-06 -6.745622e-06 -5.099900e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581     -0.000002 -1.617287e-06 -1.048326e-06 -7.403697e-07 -5.174351e-07   \n",
       "1933582     -0.000001 -8.423171e-07 -5.287683e-07 -3.735088e-07 -2.494098e-07   \n",
       "1933583     -0.000002 -1.091450e-06 -7.017644e-07 -4.857558e-07 -3.320042e-07   \n",
       "1933584     -0.000003 -2.770258e-06 -2.664968e-06 -2.564681e-06 -2.450600e-06   \n",
       "1933585     -0.000007 -6.650994e-06 -6.040530e-06 -5.525338e-06 -4.988898e-06   \n",
       "\n",
       "         dcR_dt_5400s  dcR_dt_6300s  dcR_dt_7200s  dcR_dt_9000s  \\\n",
       "0       -4.761265e-06 -4.414937e-06 -4.081192e-06 -3.643186e-06   \n",
       "1       -1.391806e-06 -1.355885e-06 -1.321234e-06 -1.272299e-06   \n",
       "2       -3.446968e-06 -3.198410e-06 -3.000881e-06 -2.754003e-06   \n",
       "3       -6.517763e-06 -5.472531e-06 -4.604982e-06 -3.765398e-06   \n",
       "4       -3.816340e-06 -3.064576e-06 -2.434986e-06 -1.922074e-06   \n",
       "...               ...           ...           ...           ...   \n",
       "1933581 -3.611708e-07 -2.649095e-07 -2.060375e-07 -1.479290e-07   \n",
       "1933582 -1.750814e-07 -1.230243e-07 -1.008656e-07 -6.518779e-08   \n",
       "1933583 -2.342006e-07 -1.665874e-07 -1.331211e-07 -9.022943e-08   \n",
       "1933584 -2.324323e-06 -2.209943e-06 -2.105090e-06 -1.964970e-06   \n",
       "1933585 -4.451762e-06 -4.016025e-06 -3.649468e-06 -3.207485e-06   \n",
       "\n",
       "         dcR_dt_10800s  dcR_dt_14400s   dcB_dt_0.1s     dcB_dt_1s  \\\n",
       "0        -3.158493e-06  -2.598647e-06 -4.088057e-06 -4.087482e-06   \n",
       "1        -1.216270e-06  -1.124400e-06 -5.439052e-07 -5.438952e-07   \n",
       "2        -2.442112e-06  -2.103576e-06 -1.841859e-06 -1.841810e-06   \n",
       "3        -2.933662e-06  -2.187034e-06 -5.681865e-05 -5.670771e-05   \n",
       "4        -1.408976e-06  -9.887752e-07 -2.939996e-04 -2.910696e-04   \n",
       "...                ...            ...           ...           ...   \n",
       "1933581  -1.012615e-07  -6.522632e-08 -2.693084e-02 -1.687926e-02   \n",
       "1933582  -5.497757e-08  -2.469737e-08 -5.997070e-02 -2.610881e-02   \n",
       "1933583  -6.402461e-08  -3.916186e-08 -4.445003e-02 -2.259184e-02   \n",
       "1933584  -1.801081e-06  -1.600227e-06 -1.606052e-06 -1.605992e-06   \n",
       "1933585  -2.745368e-06  -2.252996e-06 -5.021573e-06 -5.020985e-06   \n",
       "\n",
       "           dcB_dt_20s    dcB_dt_40s    dcB_dt_60s   dcB_dt_120s   dcB_dt_180s  \\\n",
       "0       -4.076105e-06 -4.054105e-06 -4.031904e-06 -3.989543e-06 -3.929817e-06   \n",
       "1       -5.437376e-07 -5.436038e-07 -5.436612e-07 -5.454349e-07 -5.510018e-07   \n",
       "2       -1.872815e-06 -2.041074e-06 -2.198241e-06 -2.439408e-06 -2.641642e-06   \n",
       "3       -5.461825e-05 -5.095575e-05 -4.752932e-05 -4.198866e-05 -3.561920e-05   \n",
       "4       -2.461608e-04 -1.900134e-04 -1.589217e-04 -1.259479e-04 -9.566008e-05   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -2.157870e-03 -3.859420e-04 -1.953459e-04 -9.417556e-05 -4.544848e-05   \n",
       "1933582 -2.051835e-03 -3.026561e-04 -1.475444e-04 -6.824514e-05 -3.143453e-05   \n",
       "1933583 -2.119731e-03 -3.345381e-04 -1.655540e-04 -7.785323e-05 -3.654473e-05   \n",
       "1933584 -1.604795e-06 -1.602455e-06 -1.600060e-06 -1.595298e-06 -1.588206e-06   \n",
       "1933585 -5.009312e-06 -4.986570e-06 -4.963431e-06 -4.917835e-06 -4.850798e-06   \n",
       "\n",
       "          dcB_dt_240s   dcB_dt_300s   dcB_dt_360s   dcB_dt_480s   dcB_dt_600s  \\\n",
       "0       -3.871746e-06 -3.815981e-06 -3.762520e-06 -3.686940e-06 -3.597357e-06   \n",
       "1       -5.563391e-07 -5.622408e-07 -5.687069e-07 -5.795348e-07 -5.979720e-07   \n",
       "2       -2.660125e-06 -2.693339e-06 -2.680059e-06 -2.630086e-06 -2.598301e-06   \n",
       "3       -3.088148e-05 -2.746091e-05 -2.442573e-05 -2.162627e-05 -1.848546e-05   \n",
       "4       -7.589700e-05 -6.180837e-05 -5.152282e-05 -4.091758e-05 -3.139888e-05   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -2.813605e-05 -1.959604e-05 -1.463252e-05 -1.035748e-05 -7.064342e-06   \n",
       "1933582 -1.886508e-05 -1.279510e-05 -9.354741e-06 -6.440185e-06 -4.279103e-06   \n",
       "1933583 -2.219240e-05 -1.521938e-05 -1.123911e-05 -7.818564e-06 -5.247987e-06   \n",
       "1933584 -1.581162e-06 -1.574169e-06 -1.567228e-06 -1.556919e-06 -1.543387e-06   \n",
       "1933585 -4.785260e-06 -4.721272e-06 -4.658962e-06 -4.568608e-06 -4.452267e-06   \n",
       "\n",
       "          dcB_dt_720s   dcB_dt_840s   dcB_dt_960s  dcB_dt_1080s  dcB_dt_1200s  \\\n",
       "0       -3.539946e-06 -3.466935e-06 -3.398494e-06 -3.334624e-06 -3.275326e-06   \n",
       "1       -6.314332e-07 -6.505051e-07 -6.678974e-07 -6.836100e-07 -6.976429e-07   \n",
       "2       -2.566829e-06 -2.535422e-06 -2.502033e-06 -2.470807e-06 -2.440061e-06   \n",
       "3       -1.638921e-05 -1.467391e-05 -1.348821e-05 -1.233597e-05 -1.148471e-05   \n",
       "4       -2.501700e-05 -2.077873e-05 -1.744801e-05 -1.515989e-05 -1.306263e-05   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -5.189360e-06 -3.996113e-06 -3.186346e-06 -2.625765e-06 -2.175694e-06   \n",
       "1933582 -3.076004e-06 -2.315004e-06 -1.824643e-06 -1.470473e-06 -1.220686e-06   \n",
       "1933583 -3.791882e-06 -2.902556e-06 -2.288366e-06 -1.852033e-06 -1.543870e-06   \n",
       "1933584 -1.530358e-06 -1.517271e-06 -1.504357e-06 -1.491615e-06 -1.479047e-06   \n",
       "1933585 -4.341797e-06 -4.235624e-06 -4.134706e-06 -4.037617e-06 -3.944617e-06   \n",
       "\n",
       "         dcB_dt_1500s  dcB_dt_1800s  dcB_dt_2400s  dcB_dt_3000s  dcB_dt_3600s  \\\n",
       "0       -3.183551e-06 -3.103984e-06 -2.986411e-06 -2.843354e-06 -2.723097e-06   \n",
       "1       -7.144549e-07 -7.342621e-07 -7.501933e-07 -7.369242e-07 -7.319874e-07   \n",
       "2       -2.387516e-06 -2.314852e-06 -2.211860e-06 -2.084543e-06 -1.983431e-06   \n",
       "3       -1.025059e-05 -8.869937e-06 -7.359510e-06 -5.922940e-06 -4.948729e-06   \n",
       "4       -1.077945e-05 -8.335224e-06 -6.151200e-06 -4.388905e-06 -3.372811e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -1.674383e-06 -1.204291e-06 -8.086435e-07 -5.241631e-07 -3.701848e-07   \n",
       "1933582 -9.141715e-07 -6.365700e-07 -4.211586e-07 -2.643841e-07 -1.867544e-07   \n",
       "1933583 -1.164501e-06 -8.233649e-07 -5.457252e-07 -3.508822e-07 -2.428779e-07   \n",
       "1933584 -1.457505e-06 -1.427596e-06 -1.385129e-06 -1.332484e-06 -1.282341e-06   \n",
       "1933585 -3.792182e-06 -3.591225e-06 -3.325497e-06 -3.020265e-06 -2.762669e-06   \n",
       "\n",
       "         dcB_dt_4500s  dcB_dt_5400s  dcB_dt_6300s  dcB_dt_7200s  dcB_dt_9000s  \\\n",
       "0       -2.564566e-06 -2.380632e-06 -2.207468e-06 -2.040596e-06 -1.821593e-06   \n",
       "1       -7.194350e-07 -6.959028e-07 -6.779425e-07 -6.606169e-07 -6.361497e-07   \n",
       "2       -1.862963e-06 -1.723484e-06 -1.599205e-06 -1.500441e-06 -1.377001e-06   \n",
       "3       -4.034726e-06 -3.258881e-06 -2.736265e-06 -2.302491e-06 -1.882699e-06   \n",
       "4       -2.549950e-06 -1.908170e-06 -1.532288e-06 -1.217493e-06 -9.610370e-07   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -2.587175e-07 -1.805854e-07 -1.324547e-07 -1.030188e-07 -7.396451e-08   \n",
       "1933582 -1.247049e-07 -8.754069e-08 -6.151214e-08 -5.043279e-08 -3.259390e-08   \n",
       "1933583 -1.660021e-07 -1.171003e-07 -8.329371e-08 -6.656053e-08 -4.511471e-08   \n",
       "1933584 -1.225300e-06 -1.162162e-06 -1.104972e-06 -1.052545e-06 -9.824848e-07   \n",
       "1933585 -2.494449e-06 -2.225881e-06 -2.008012e-06 -1.824734e-06 -1.603742e-06   \n",
       "\n",
       "         dcB_dt_10800s  dcB_dt_14400s  dcC_dt_0.1s  dcC_dt_1s  dcC_dt_20s  \\\n",
       "0        -1.579247e-06  -1.299324e-06    -0.000008  -0.000008   -0.000008   \n",
       "1        -6.081352e-07  -5.622000e-07    -0.000001  -0.000001   -0.000001   \n",
       "2        -1.221056e-06  -1.051788e-06    -0.000004  -0.000004   -0.000004   \n",
       "3        -1.466831e-06  -1.093517e-06    -0.000114  -0.000113   -0.000109   \n",
       "4        -7.044882e-07  -4.943876e-07    -0.000588  -0.000582   -0.000485   \n",
       "...                ...            ...          ...        ...         ...   \n",
       "1933581  -5.063075e-08  -3.261316e-08    -0.026931  -0.016879   -0.002158   \n",
       "1933582  -2.748878e-08  -1.234869e-08    -0.059971  -0.026109   -0.002052   \n",
       "1933583  -3.201230e-08  -1.958093e-08    -0.044450  -0.022592   -0.002120   \n",
       "1933584  -9.005405e-07  -8.001134e-07    -0.000002  -0.000002   -0.000002   \n",
       "1933585  -1.372684e-06  -1.126498e-06    -0.000005  -0.000005   -0.000005   \n",
       "\n",
       "         dcC_dt_40s  dcC_dt_60s  dcC_dt_120s  dcC_dt_180s  dcC_dt_240s  \\\n",
       "0         -0.000008   -0.000008    -0.000008    -0.000008    -0.000008   \n",
       "1         -0.000001   -0.000001    -0.000001    -0.000001    -0.000001   \n",
       "2         -0.000003   -0.000003    -0.000003    -0.000003    -0.000003   \n",
       "3         -0.000102   -0.000095    -0.000083    -0.000070    -0.000059   \n",
       "4         -0.000349   -0.000261    -0.000166    -0.000097    -0.000067   \n",
       "...             ...         ...          ...          ...          ...   \n",
       "1933581   -0.000386   -0.000195    -0.000094    -0.000045    -0.000028   \n",
       "1933582   -0.000303   -0.000148    -0.000068    -0.000031    -0.000019   \n",
       "1933583   -0.000335   -0.000166    -0.000078    -0.000037    -0.000022   \n",
       "1933584   -0.000002   -0.000002    -0.000002    -0.000002    -0.000002   \n",
       "1933585   -0.000005   -0.000005    -0.000005    -0.000005    -0.000005   \n",
       "\n",
       "         dcC_dt_300s  dcC_dt_360s  dcC_dt_480s  dcC_dt_600s   dcC_dt_720s  \\\n",
       "0          -0.000008    -0.000007    -0.000007    -0.000007 -6.793569e-06   \n",
       "1          -0.000001    -0.000001    -0.000001    -0.000001 -9.612863e-07   \n",
       "2          -0.000003    -0.000003    -0.000003    -0.000003 -2.557647e-06   \n",
       "3          -0.000051    -0.000044    -0.000038    -0.000030 -2.491907e-05   \n",
       "4          -0.000051    -0.000041    -0.000032    -0.000024 -1.919403e-05   \n",
       "...              ...          ...          ...          ...           ...   \n",
       "1933581    -0.000020    -0.000015    -0.000010    -0.000007 -5.189360e-06   \n",
       "1933582    -0.000013    -0.000009    -0.000006    -0.000004 -3.076004e-06   \n",
       "1933583    -0.000015    -0.000011    -0.000008    -0.000005 -3.791882e-06   \n",
       "1933584    -0.000002    -0.000002    -0.000002    -0.000002 -1.530358e-06   \n",
       "1933585    -0.000005    -0.000005    -0.000005    -0.000004 -4.341797e-06   \n",
       "\n",
       "          dcC_dt_840s   dcC_dt_960s  dcC_dt_1080s  dcC_dt_1200s  dcC_dt_1500s  \\\n",
       "0       -6.568142e-06 -6.350111e-06 -6.139477e-06 -5.936238e-06 -5.599986e-06   \n",
       "1       -9.354279e-07 -9.113359e-07 -8.890104e-07 -8.684511e-07 -8.406147e-07   \n",
       "2       -2.522532e-06 -2.497260e-06 -2.466071e-06 -2.435363e-06 -2.382886e-06   \n",
       "3       -2.077705e-05 -1.796922e-05 -1.531546e-05 -1.349336e-05 -1.101066e-05   \n",
       "4       -1.599939e-05 -1.347410e-05 -1.175267e-05 -1.016580e-05 -8.431428e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -3.996113e-06 -3.186346e-06 -2.625765e-06 -2.175694e-06 -1.674383e-06   \n",
       "1933582 -2.315004e-06 -1.824643e-06 -1.470473e-06 -1.220686e-06 -9.141715e-07   \n",
       "1933583 -2.902556e-06 -2.288366e-06 -1.852033e-06 -1.543870e-06 -1.164501e-06   \n",
       "1933584 -1.517271e-06 -1.504357e-06 -1.491615e-06 -1.479047e-06 -1.457505e-06   \n",
       "1933585 -4.235624e-06 -4.134706e-06 -4.037617e-06 -3.944617e-06 -3.792182e-06   \n",
       "\n",
       "         dcC_dt_1800s  dcC_dt_2400s  dcC_dt_3000s  dcC_dt_3600s  dcC_dt_4500s  \\\n",
       "0       -5.169222e-06 -4.591182e-06 -3.954645e-06 -3.451027e-06 -2.952049e-06   \n",
       "1       -8.052795e-07 -7.666475e-07 -7.510054e-07 -7.277486e-07 -7.067006e-07   \n",
       "2       -2.310321e-06 -2.207481e-06 -2.080373e-06 -1.979543e-06 -1.859388e-06   \n",
       "3       -8.548837e-06 -6.261499e-06 -4.486096e-06 -3.539266e-06 -2.718801e-06   \n",
       "4       -6.565622e-06 -4.882889e-06 -3.512654e-06 -2.714331e-06 -2.063870e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581 -1.204291e-06 -8.086435e-07 -5.241631e-07 -3.701848e-07 -2.587175e-07   \n",
       "1933582 -6.365700e-07 -4.211586e-07 -2.643841e-07 -1.867544e-07 -1.247049e-07   \n",
       "1933583 -8.233649e-07 -5.457252e-07 -3.508822e-07 -2.428779e-07 -1.660021e-07   \n",
       "1933584 -1.427596e-06 -1.385129e-06 -1.332484e-06 -1.282341e-06 -1.225300e-06   \n",
       "1933585 -3.591225e-06 -3.325497e-06 -3.020265e-06 -2.762669e-06 -2.494449e-06   \n",
       "\n",
       "         dcC_dt_5400s  dcC_dt_6300s  dcC_dt_7200s  dcC_dt_9000s  \\\n",
       "0       -2.526608e-06 -2.165654e-06 -1.963749e-06 -1.654686e-06   \n",
       "1       -6.921373e-07 -6.730992e-07 -6.549831e-07 -6.300194e-07   \n",
       "2       -1.720214e-06 -1.596206e-06 -1.497652e-06 -1.374472e-06   \n",
       "3       -2.122144e-06 -1.760745e-06 -1.463205e-06 -1.195342e-06   \n",
       "4       -1.552999e-06 -1.251059e-06 -9.982177e-07 -7.900777e-07   \n",
       "...               ...           ...           ...           ...   \n",
       "1933581 -1.805854e-07 -1.324547e-07 -1.030188e-07 -7.396451e-08   \n",
       "1933582 -8.754069e-08 -6.151214e-08 -5.043279e-08 -3.259390e-08   \n",
       "1933583 -1.171003e-07 -8.329371e-08 -6.656053e-08 -4.511471e-08   \n",
       "1933584 -1.162162e-06 -1.104972e-06 -1.052545e-06 -9.824848e-07   \n",
       "1933585 -2.225881e-06 -2.008012e-06 -1.824734e-06 -1.603742e-06   \n",
       "\n",
       "         dcC_dt_10800s  dcC_dt_14400s   dcP_dt_0.1s     dcP_dt_1s  \\\n",
       "0        -1.402252e-06  -1.129411e-06  4.088057e-06  4.087481e-06   \n",
       "1        -6.034303e-07  -5.578604e-07  5.439052e-07  5.438951e-07   \n",
       "2        -1.218855e-06  -1.049931e-06  1.841858e-06  1.841741e-06   \n",
       "3        -9.317805e-07  -7.001592e-07  5.681865e-05  5.670770e-05   \n",
       "4        -5.816795e-07  -4.097659e-07  2.939995e-04  2.910601e-04   \n",
       "...                ...            ...           ...           ...   \n",
       "1933581  -5.063075e-08  -3.261316e-08  5.048965e-06  3.618891e-05   \n",
       "1933582  -2.748878e-08  -1.234869e-08  2.341640e-03  9.737870e-03   \n",
       "1933583  -3.201230e-08  -1.958093e-08  3.870968e-02  2.294981e-02   \n",
       "1933584  -9.005405e-07  -8.001134e-07  0.000000e+00  4.996004e-15   \n",
       "1933585  -1.372684e-06  -1.126498e-06  1.110223e-14  1.232872e-12   \n",
       "\n",
       "           dcP_dt_20s    dcP_dt_40s    dcP_dt_60s   dcP_dt_120s   dcP_dt_180s  \\\n",
       "0        4.076068e-06  4.053858e-06  4.031280e-06  3.987132e-06  3.922839e-06   \n",
       "1        5.436924e-07  5.432954e-07  5.428889e-07  5.420773e-07  5.408632e-07   \n",
       "2        1.839423e-06  1.834932e-06  1.830404e-06  1.821649e-06  1.808893e-06   \n",
       "3        5.461164e-05  5.091377e-05  4.743872e-05  4.174738e-05  3.508935e-05   \n",
       "4        2.438683e-04  1.797679e-04  1.399830e-04  9.740166e-05  6.436413e-05   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581  7.893133e-05  6.790235e-05  5.789630e-05  4.620172e-05  3.593379e-05   \n",
       "1933582  2.945537e-03  3.926387e-04  1.749456e-04  7.728380e-05  3.456667e-05   \n",
       "1933583  2.131644e-03  3.350985e-04  1.657595e-04  7.792788e-05  3.657216e-05   \n",
       "1933584  1.981674e-12  1.324082e-11  3.512193e-11  1.279272e-10  3.620355e-10   \n",
       "1933585  5.031335e-10  3.389474e-09  8.541778e-09  2.921315e-08  7.787188e-08   \n",
       "\n",
       "          dcP_dt_240s   dcP_dt_300s   dcP_dt_360s   dcP_dt_480s   dcP_dt_600s  \\\n",
       "0        3.859738e-06  3.798085e-06  3.737882e-06  3.650476e-06  3.540130e-06   \n",
       "1        5.396584e-07  5.384619e-07  5.372737e-07  5.355079e-07  5.331836e-07   \n",
       "2        1.796485e-06  1.784196e-06  1.772151e-06  1.754402e-06  1.731042e-06   \n",
       "3        3.003931e-05  2.629489e-05  2.297001e-05  1.976592e-05  1.619324e-05   \n",
       "4        4.773124e-05  3.754923e-05  3.082600e-05  2.423206e-05  1.850668e-05   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581  2.979256e-05  2.559779e-05  2.250896e-05  1.917133e-05  1.598445e-05   \n",
       "1933582  2.049380e-05  1.380191e-05  1.004454e-05  6.884557e-06  4.555979e-06   \n",
       "1933583  2.220696e-05  1.522851e-05  1.124544e-05  7.822682e-06  5.250572e-06   \n",
       "1933584  6.625589e-10  1.040628e-09  1.496242e-09  2.334753e-09  3.855287e-09   \n",
       "1933585  1.363369e-07  2.072708e-07  2.980277e-07  4.597933e-07  6.570668e-07   \n",
       "\n",
       "          dcP_dt_720s   dcP_dt_840s   dcP_dt_960s  dcP_dt_1080s  dcP_dt_1200s  \\\n",
       "0        3.444505e-06  3.345026e-06  3.249535e-06  3.158034e-06  3.070521e-06   \n",
       "1        5.309065e-07  5.286444e-07  5.264111e-07  5.242068e-07  5.220313e-07   \n",
       "2        1.708159e-06  1.685985e-06  1.666431e-06  1.645626e-06  1.625141e-06   \n",
       "3        1.376943e-05  1.181699e-05  1.048581e-05  9.217145e-06  8.326024e-06   \n",
       "4        1.473701e-05  1.225938e-05  1.030737e-05  8.970855e-06  7.742810e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581  1.372577e-05  1.202645e-05  1.069821e-05  9.648936e-06  8.750986e-06   \n",
       "1933582  3.266368e-06  2.453337e-06  1.931095e-06  1.554397e-06  1.289325e-06   \n",
       "1933583  3.793661e-06  2.903872e-06  2.289373e-06  1.852830e-06  1.544523e-06   \n",
       "1933584  6.703858e-09  8.955507e-09  1.140323e-08  1.404702e-08  1.688687e-08   \n",
       "1933585  8.787266e-07  1.062099e-06  1.228172e-06  1.366187e-06  1.474165e-06   \n",
       "\n",
       "         dcP_dt_1500s  dcP_dt_1800s  dcP_dt_2400s  dcP_dt_3000s  dcP_dt_3600s  \\\n",
       "0        2.927846e-06  2.757735e-06  2.525864e-06  2.266000e-06  2.058041e-06   \n",
       "1        5.183565e-07  5.131805e-07  5.056136e-07  4.959765e-07  4.865787e-07   \n",
       "2        1.590134e-06  1.541724e-06  1.473114e-06  1.388305e-06  1.320991e-06   \n",
       "3        7.087083e-06  5.806258e-06  4.540336e-06  3.469679e-06  2.829332e-06   \n",
       "4        6.403626e-06  4.966949e-06  3.678030e-06  2.633853e-06  2.029047e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581  7.573498e-06  6.314217e-06  5.047843e-06  3.943243e-06  3.222267e-06   \n",
       "1933582  9.643738e-07  6.706301e-07  4.431959e-07  2.779091e-07  1.961900e-07   \n",
       "1933583  1.164981e-06  8.236949e-07  5.459379e-07  3.510157e-07  2.429686e-07   \n",
       "1933584  2.237131e-08  3.118657e-08  4.773625e-08  7.528999e-08  1.009812e-07   \n",
       "1933585  1.596304e-06  1.677470e-06  1.676404e-06  1.586956e-06  1.467840e-06   \n",
       "\n",
       "         dcP_dt_4500s  dcP_dt_5400s  dcP_dt_6300s  dcP_dt_7200s  dcP_dt_9000s  \\\n",
       "0        1.838872e-06  1.635747e-06  1.457707e-06  1.334782e-06  1.158760e-06   \n",
       "1        4.753785e-07  4.626800e-07  4.503472e-07  4.385333e-07  4.220564e-07   \n",
       "2        1.240783e-06  1.147899e-06  1.065137e-06  9.993644e-07  9.171579e-07   \n",
       "3        2.251176e-06  1.793675e-06  1.499003e-06  1.255232e-06  1.026014e-06   \n",
       "4        1.537940e-06  1.153723e-06  9.277823e-07  7.385703e-07  5.837049e-07   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1933581  2.612146e-06  2.112389e-06  1.759699e-06  1.509251e-06  1.236242e-06   \n",
       "1933582  1.309093e-07  9.186239e-08  6.451554e-08  5.289476e-08  3.416669e-08   \n",
       "1933583  1.660631e-07  1.171430e-07  8.332367e-08  6.658441e-08  4.513072e-08   \n",
       "1933584  1.374318e-07  1.793391e-07  2.180296e-07  2.523556e-07  2.923334e-07   \n",
       "1933585  1.331547e-06  1.188640e-06  1.070695e-06  9.719106e-07  8.527131e-07   \n",
       "\n",
       "         dcP_dt_10800s  dcP_dt_14400s  dcIMP1_dt_0.1s  dcIMP1_dt_1s  \\\n",
       "0         9.938329e-07   8.095783e-07        0.000000      0.000000   \n",
       "1         4.038552e-07   3.733535e-07        0.000000      0.000000   \n",
       "2         8.133037e-07   7.005731e-07        0.000000      0.000000   \n",
       "3         7.995371e-07   5.978920e-07        0.000000      0.000000   \n",
       "4         4.287226e-07   3.013845e-07        0.000000      0.000000   \n",
       "...                ...            ...             ...           ...   \n",
       "1933581   9.840276e-07   7.509900e-07        0.026931      0.016879   \n",
       "1933582   2.881831e-08   1.293586e-08        0.059971      0.026109   \n",
       "1933583   3.202362e-08   1.958779e-08        0.044450      0.022592   \n",
       "1933584   3.294262e-07   3.521854e-07        0.000002      0.000002   \n",
       "1933585   7.283900e-07   5.963261e-07        0.000005      0.000005   \n",
       "\n",
       "         dcIMP1_dt_20s  dcIMP1_dt_40s  dcIMP1_dt_60s  dcIMP1_dt_120s  \\\n",
       "0             0.000000       0.000000       0.000000        0.000000   \n",
       "1             0.000000       0.000000       0.000000        0.000000   \n",
       "2             0.000000       0.000000       0.000000        0.000000   \n",
       "3             0.000000       0.000000       0.000000        0.000000   \n",
       "4             0.000000       0.000000       0.000000        0.000000   \n",
       "...                ...            ...            ...             ...   \n",
       "1933581       0.002158       0.000386       0.000195        0.000094   \n",
       "1933582       0.002052       0.000303       0.000148        0.000068   \n",
       "1933583       0.002120       0.000335       0.000166        0.000078   \n",
       "1933584       0.000002       0.000002       0.000002        0.000002   \n",
       "1933585       0.000005       0.000005       0.000005        0.000005   \n",
       "\n",
       "         dcIMP1_dt_180s  dcIMP1_dt_240s  dcIMP1_dt_300s  dcIMP1_dt_360s  \\\n",
       "0              0.000000        0.000000        0.000000        0.000000   \n",
       "1              0.000000        0.000000        0.000000        0.000000   \n",
       "2              0.000000        0.000000        0.000000        0.000000   \n",
       "3              0.000000        0.000000        0.000000        0.000000   \n",
       "4              0.000000        0.000000        0.000000        0.000000   \n",
       "...                 ...             ...             ...             ...   \n",
       "1933581        0.000045        0.000028        0.000020        0.000015   \n",
       "1933582        0.000031        0.000019        0.000013        0.000009   \n",
       "1933583        0.000037        0.000022        0.000015        0.000011   \n",
       "1933584        0.000002        0.000002        0.000002        0.000002   \n",
       "1933585        0.000005        0.000005        0.000005        0.000005   \n",
       "\n",
       "         dcIMP1_dt_480s  dcIMP1_dt_600s  dcIMP1_dt_720s  dcIMP1_dt_840s  \\\n",
       "0              0.000000        0.000000        0.000000        0.000000   \n",
       "1              0.000000        0.000000        0.000000        0.000000   \n",
       "2              0.000000        0.000000        0.000000        0.000000   \n",
       "3              0.000000        0.000000        0.000000        0.000000   \n",
       "4              0.000000        0.000000        0.000000        0.000000   \n",
       "...                 ...             ...             ...             ...   \n",
       "1933581        0.000010        0.000007        0.000005        0.000004   \n",
       "1933582        0.000006        0.000004        0.000003        0.000002   \n",
       "1933583        0.000008        0.000005        0.000004        0.000003   \n",
       "1933584        0.000002        0.000002        0.000002        0.000002   \n",
       "1933585        0.000005        0.000004        0.000004        0.000004   \n",
       "\n",
       "         dcIMP1_dt_960s  dcIMP1_dt_1080s  dcIMP1_dt_1200s  dcIMP1_dt_1500s  \\\n",
       "0              0.000000         0.000000         0.000000     0.000000e+00   \n",
       "1              0.000000         0.000000         0.000000     0.000000e+00   \n",
       "2              0.000000         0.000000         0.000000     0.000000e+00   \n",
       "3              0.000000         0.000000         0.000000     0.000000e+00   \n",
       "4              0.000000         0.000000         0.000000     0.000000e+00   \n",
       "...                 ...              ...              ...              ...   \n",
       "1933581        0.000003         0.000003         0.000002     1.674383e-06   \n",
       "1933582        0.000002         0.000001         0.000001     9.141715e-07   \n",
       "1933583        0.000002         0.000002         0.000002     1.164501e-06   \n",
       "1933584        0.000002         0.000001         0.000001     1.457505e-06   \n",
       "1933585        0.000004         0.000004         0.000004     3.792182e-06   \n",
       "\n",
       "         dcIMP1_dt_1800s  dcIMP1_dt_2400s  dcIMP1_dt_3000s  dcIMP1_dt_3600s  \\\n",
       "0           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "2           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "3           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "4           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581     1.204291e-06     8.086435e-07     5.241631e-07     3.701848e-07   \n",
       "1933582     6.365700e-07     4.211586e-07     2.643841e-07     1.867544e-07   \n",
       "1933583     8.233649e-07     5.457252e-07     3.508822e-07     2.428779e-07   \n",
       "1933584     1.427596e-06     1.385129e-06     1.332484e-06     1.282341e-06   \n",
       "1933585     3.591225e-06     3.325497e-06     3.020265e-06     2.762669e-06   \n",
       "\n",
       "         dcIMP1_dt_4500s  dcIMP1_dt_5400s  dcIMP1_dt_6300s  dcIMP1_dt_7200s  \\\n",
       "0           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "2           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "3           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "4           0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581     2.587175e-07     1.805854e-07     1.324547e-07     1.030188e-07   \n",
       "1933582     1.247049e-07     8.754069e-08     6.151214e-08     5.043279e-08   \n",
       "1933583     1.660021e-07     1.171003e-07     8.329371e-08     6.656053e-08   \n",
       "1933584     1.225300e-06     1.162162e-06     1.104972e-06     1.052545e-06   \n",
       "1933585     2.494449e-06     2.225881e-06     2.008012e-06     1.824734e-06   \n",
       "\n",
       "         dcIMP1_dt_9000s  dcIMP1_dt_10800s  dcIMP1_dt_14400s  dcIMP2_dt_0.1s  \\\n",
       "0           0.000000e+00      0.000000e+00      0.000000e+00    8.070481e-16   \n",
       "1           0.000000e+00      0.000000e+00      0.000000e+00    9.785989e-16   \n",
       "2           0.000000e+00      0.000000e+00      0.000000e+00    6.089422e-13   \n",
       "3           0.000000e+00      0.000000e+00      0.000000e+00    1.414843e-13   \n",
       "4           0.000000e+00      0.000000e+00      0.000000e+00    7.615493e-11   \n",
       "...                  ...               ...               ...             ...   \n",
       "1933581     7.396451e-08      5.063075e-08      3.261316e-08    0.000000e+00   \n",
       "1933582     3.259390e-08      2.748878e-08      1.234869e-08    0.000000e+00   \n",
       "1933583     4.511471e-08      3.201230e-08      1.958093e-08    0.000000e+00   \n",
       "1933584     9.824848e-07      9.005405e-07      8.001134e-07    0.000000e+00   \n",
       "1933585     1.603742e-06      1.372684e-06      1.126498e-06    0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_1s  dcIMP2_dt_20s  dcIMP2_dt_40s  dcIMP2_dt_60s  \\\n",
       "0        8.980895e-14   3.668111e-11   2.474456e-10   6.234694e-10   \n",
       "1        1.086737e-13   4.527918e-11   3.084362e-10   7.722838e-10   \n",
       "2        6.905869e-11   3.339178e-08   2.061412e-07   3.678370e-07   \n",
       "3        1.621683e-11   6.615895e-09   4.198782e-08   9.060069e-08   \n",
       "4        9.568366e-09   2.292423e-06   1.024557e-05   1.893868e-05   \n",
       "...               ...            ...            ...            ...   \n",
       "1933581  0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "1933582  0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "1933583  0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "1933584  0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "1933585  0.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_120s  dcIMP2_dt_180s  dcIMP2_dt_240s  dcIMP2_dt_300s  \\\n",
       "0          2.410375e-09    6.978181e-09    1.200882e-08    1.789541e-08   \n",
       "1          3.357575e-09    1.013860e-08    1.668071e-08    2.377892e-08   \n",
       "2          6.177591e-07    8.327485e-07    8.636401e-07    9.091432e-07   \n",
       "3          2.412777e-07    5.298501e-07    8.421764e-07    1.166023e-06   \n",
       "4          2.854621e-05    3.129595e-05    2.816576e-05    2.425914e-05   \n",
       "...                 ...             ...             ...             ...   \n",
       "1933581    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933582    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933583    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933584    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933585    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_360s  dcIMP2_dt_480s  dcIMP2_dt_600s  dcIMP2_dt_720s  \\\n",
       "0          2.463798e-08    3.646376e-08    5.722752e-08    9.544097e-08   \n",
       "1          3.143323e-08    4.402689e-08    6.478838e-08    1.005267e-07   \n",
       "2          9.079079e-07    8.756841e-07    8.672594e-07    8.586704e-07   \n",
       "3          1.455722e-06    1.860349e-06    2.292223e-06    2.619782e-06   \n",
       "4          2.069682e-05    1.668552e-05    1.289220e-05    1.027999e-05   \n",
       "...                 ...             ...             ...             ...   \n",
       "1933581    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933582    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933583    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933584    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "1933585    0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_840s  dcIMP2_dt_960s  dcIMP2_dt_1080s  dcIMP2_dt_1200s  \\\n",
       "0          1.219090e-07    1.489589e-07     1.765906e-07     2.048042e-07   \n",
       "1          1.218608e-07    1.414863e-07     1.594032e-07     1.756115e-07   \n",
       "2          8.494373e-07    8.356024e-07     8.251810e-07     8.149195e-07   \n",
       "3          2.856926e-06    3.002398e-06     3.118826e-06     3.158690e-06   \n",
       "4          8.519358e-06    7.140639e-06     6.189038e-06     5.319817e-06   \n",
       "...                 ...             ...              ...              ...   \n",
       "1933581    0.000000e+00    0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933582    0.000000e+00    0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933583    0.000000e+00    0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933584    0.000000e+00    0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933585    0.000000e+00    0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_1500s  dcIMP2_dt_1800s  dcIMP2_dt_2400s  dcIMP2_dt_3000s  \\\n",
       "0           2.557053e-07     3.462486e-07     4.605467e-07     5.773542e-07   \n",
       "1           1.960984e-07     2.210816e-07     2.445797e-07     2.409476e-07   \n",
       "2           7.973819e-07     7.731279e-07     7.387467e-07     6.962378e-07   \n",
       "3           3.163510e-06     3.063679e-06     2.819174e-06     2.453261e-06   \n",
       "4           4.375824e-06     3.368276e-06     2.473170e-06     1.755052e-06   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933582     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933583     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933584     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933585     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_3600s  dcIMP2_dt_4500s  dcIMP2_dt_5400s  dcIMP2_dt_6300s  \\\n",
       "0           6.650559e-07     7.256945e-07     7.448855e-07     7.497610e-07   \n",
       "1           2.454087e-07     2.440565e-07     2.332228e-07     2.275953e-07   \n",
       "2           6.624394e-07     6.221791e-07     5.755844e-07     5.340681e-07   \n",
       "3           2.119397e-06     1.783551e-06     1.465206e-06     1.237262e-06   \n",
       "4           1.343764e-06     1.012010e-06     7.544470e-07     6.045058e-07   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933582     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933583     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933584     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "1933585     0.000000e+00     0.000000e+00     0.000000e+00     0.000000e+00   \n",
       "\n",
       "         dcIMP2_dt_7200s  dcIMP2_dt_9000s  dcIMP2_dt_10800s  dcIMP2_dt_14400s  \\\n",
       "0           7.058142e-07     6.628333e-07      5.854136e-07      4.897452e-07   \n",
       "1           2.220835e-07     2.140933e-07      2.042801e-07      1.888465e-07   \n",
       "2           5.010763e-07     4.598434e-07      4.077525e-07      3.512150e-07   \n",
       "3           1.047259e-06     8.566856e-07      6.672937e-07      4.956248e-07   \n",
       "4           4.789229e-07     3.773321e-07      2.757656e-07      1.930031e-07   \n",
       "...                  ...              ...               ...               ...   \n",
       "1933581     0.000000e+00     0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "1933582     0.000000e+00     0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "1933583     0.000000e+00     0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "1933584     0.000000e+00     0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "1933585     0.000000e+00     0.000000e+00      0.000000e+00      0.000000e+00   \n",
       "\n",
       "         dcINT1_dt_0.1s  dcINT1_dt_1s  dcINT1_dt_20s  dcINT1_dt_40s  \\\n",
       "0          4.088057e-06  4.087481e-06   4.075995e-06   4.053363e-06   \n",
       "1          5.439052e-07  5.438948e-07   5.436018e-07   5.426785e-07   \n",
       "2          1.841857e-06  1.841603e-06   1.772640e-06   1.422650e-06   \n",
       "3          5.681865e-05  5.670766e-05   5.459840e-05   5.082979e-05   \n",
       "4          2.939994e-04  2.910409e-04   2.392835e-04   1.592767e-04   \n",
       "...                 ...           ...            ...            ...   \n",
       "1933581    2.692579e-02  1.684307e-02   2.078939e-03   3.180396e-04   \n",
       "1933582    5.762906e-02  1.637094e-02  -8.937028e-04  -8.998255e-05   \n",
       "1933583    5.740351e-03 -3.579727e-04  -1.191277e-05  -5.603838e-07   \n",
       "1933584    1.606052e-06  1.605992e-06   1.604791e-06   1.602428e-06   \n",
       "1933585    5.021573e-06  5.020982e-06   5.008306e-06   4.979791e-06   \n",
       "\n",
       "         dcINT1_dt_60s  dcINT1_dt_120s  dcINT1_dt_180s  dcINT1_dt_240s  \\\n",
       "0         4.030033e-06    3.982312e-06    3.908883e-06    3.835720e-06   \n",
       "1         5.413444e-07    5.353621e-07    5.205860e-07    5.062970e-07   \n",
       "2         1.094730e-06    5.861311e-07    1.433965e-07    6.920428e-08   \n",
       "3         4.725752e-05    4.126483e-05    3.402965e-05    2.835495e-05   \n",
       "4         1.021057e-04    4.030923e-05    1.772224e-06   -8.600281e-06   \n",
       "...                ...             ...             ...             ...   \n",
       "1933581   1.374495e-04    4.797383e-05    9.514680e-06   -1.656507e-06   \n",
       "1933582  -2.740118e-05   -9.038659e-06   -3.132139e-06   -1.628720e-06   \n",
       "1933583  -2.055346e-07   -7.465373e-08   -2.743580e-08   -1.456619e-08   \n",
       "1933584   1.599990e-06    1.595042e-06    1.587482e-06    1.579836e-06   \n",
       "1933585   4.946347e-06    4.859409e-06    4.695054e-06    4.512587e-06   \n",
       "\n",
       "         dcINT1_dt_300s  dcINT1_dt_360s  dcINT1_dt_480s  dcINT1_dt_600s  \\\n",
       "0          3.762294e-06    3.688606e-06    3.577549e-06    3.425675e-06   \n",
       "1          4.909040e-07    4.744072e-07    4.474542e-07    4.036069e-07   \n",
       "2         -3.409013e-08   -4.366480e-08    3.033687e-09   -3.476988e-09   \n",
       "3          2.396284e-05    2.005857e-05    1.604522e-05    1.160879e-05   \n",
       "4         -1.096905e-05   -1.056764e-05   -9.138992e-06   -7.277729e-06   \n",
       "...                 ...             ...             ...             ...   \n",
       "1933581   -6.001757e-06   -7.876447e-06   -8.813852e-06   -8.920112e-06   \n",
       "1933582   -1.006818e-06   -6.897982e-07   -4.443722e-07   -2.768760e-07   \n",
       "1933583   -9.138905e-09   -6.334045e-09   -4.117715e-09   -2.584261e-09   \n",
       "1933584    1.572088e-06    1.564235e-06    1.552249e-06    1.535677e-06   \n",
       "1933585    4.306731e-06    4.062907e-06    3.649021e-06    3.138133e-06   \n",
       "\n",
       "         dcINT1_dt_720s  dcINT1_dt_840s  dcINT1_dt_960s  dcINT1_dt_1080s  \\\n",
       "0          3.253623e-06    3.101208e-06    2.951617e-06     2.804852e-06   \n",
       "1          3.298532e-07    2.849228e-07    2.434385e-07     2.054004e-07   \n",
       "2         -9.181863e-09   -1.288997e-08   -4.773819e-09    -4.735937e-09   \n",
       "3          8.529863e-06    6.103138e-06    4.481014e-06     2.979493e-06   \n",
       "4         -5.822967e-06   -4.779341e-06   -3.973909e-06    -3.407220e-06   \n",
       "...                 ...             ...             ...              ...   \n",
       "1933581   -8.536409e-06   -8.030337e-06   -7.511859e-06    -7.023172e-06   \n",
       "1933582   -1.903640e-07   -1.383322e-07   -1.064525e-07    -8.392418e-08   \n",
       "1933583   -1.779006e-09   -1.316455e-09   -1.007839e-09    -7.969111e-10   \n",
       "1933584    1.516950e-06    1.499360e-06    1.481550e-06     1.463521e-06   \n",
       "1933585    2.584344e-06    2.111426e-06    1.678362e-06     1.305243e-06   \n",
       "\n",
       "         dcINT1_dt_1200s  dcINT1_dt_1500s  dcINT1_dt_1800s  dcINT1_dt_2400s  \\\n",
       "0           2.660913e-06     2.416435e-06     2.065238e-06     1.604771e-06   \n",
       "1           1.708083e-07     1.261598e-07     7.101734e-08     1.645423e-08   \n",
       "2          -4.697721e-09    -4.629965e-09    -4.531498e-09    -4.379616e-09   \n",
       "3           2.008643e-06     7.600633e-07    -3.211001e-07    -1.098012e-06   \n",
       "4          -2.896824e-06    -2.348021e-06    -1.769602e-06    -1.268311e-06   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581    -6.575293e-06    -5.899115e-06    -5.109926e-06    -4.239199e-06   \n",
       "1933582    -6.863888e-08    -5.020226e-08    -3.406008e-08    -2.203734e-08   \n",
       "1933583    -6.525056e-10    -4.798276e-10    -3.300045e-10    -2.127637e-10   \n",
       "1933584     1.445273e-06     1.412763e-06     1.365223e-06     1.289656e-06   \n",
       "1933585     9.962871e-07     5.995745e-07     2.362847e-07    -2.731091e-08   \n",
       "\n",
       "         dcINT1_dt_3000s  dcINT1_dt_3600s  dcINT1_dt_4500s  dcINT1_dt_5400s  \\\n",
       "0           1.111291e-06     7.279296e-07     3.874827e-07     1.459760e-07   \n",
       "1           1.408128e-08    -4.238847e-09    -1.273443e-08    -3.765499e-09   \n",
       "2          -4.170138e-09    -3.887444e-09    -3.574807e-09    -3.269382e-09   \n",
       "3          -1.436844e-06    -1.409463e-06    -1.315926e-06    -1.136737e-06   \n",
       "4          -8.762507e-07    -6.584799e-07    -4.860797e-07    -3.551712e-07   \n",
       "...                  ...              ...              ...              ...   \n",
       "1933581    -3.419080e-06    -2.852082e-06    -2.353428e-06    -1.931804e-06   \n",
       "1933582    -1.352491e-08    -9.435631e-09    -6.204366e-09    -4.321705e-09   \n",
       "1933583    -1.335217e-10    -9.072850e-11    -6.105283e-11    -4.264407e-11   \n",
       "1933584     1.181904e-06     1.080378e-06     9.504362e-07     8.034834e-07   \n",
       "1933585    -1.536468e-07    -1.730115e-07    -1.686449e-07    -1.513983e-07   \n",
       "\n",
       "         dcINT1_dt_6300s  dcINT1_dt_7200s  dcINT1_dt_9000s  dcINT1_dt_10800s  \\\n",
       "0          -4.181475e-08    -7.684659e-08    -1.669067e-07     -1.769943e-07   \n",
       "1          -4.843341e-09    -5.633726e-09    -6.130266e-09     -4.704961e-09   \n",
       "2          -2.999107e-09    -2.788279e-09    -2.528926e-09     -2.201280e-09   \n",
       "3          -9.755206e-07    -8.392859e-07    -6.873577e-07     -5.350503e-07   \n",
       "4          -2.812293e-07    -2.192755e-07    -1.709593e-07     -1.228087e-07   \n",
       "...                  ...              ...              ...               ...   \n",
       "1933581    -1.627244e-06    -1.406232e-06    -1.162278e-06     -9.333969e-07   \n",
       "1933582    -3.003400e-09    -2.461975e-09    -1.572789e-09     -1.329523e-09   \n",
       "1933583    -2.996441e-11    -2.388107e-11    -1.600795e-11     -1.131273e-11   \n",
       "1933584     6.689125e-07     5.478341e-07     3.978180e-07      2.416882e-07   \n",
       "1933585    -1.333769e-07    -1.190871e-07    -1.016838e-07     -8.409586e-08   \n",
       "\n",
       "         dcINT1_dt_14400s  \n",
       "0           -1.699122e-07  \n",
       "1           -4.339602e-09  \n",
       "2           -1.856842e-09  \n",
       "3           -3.933577e-07  \n",
       "4           -8.462171e-08  \n",
       "...                   ...  \n",
       "1933581     -7.183769e-07  \n",
       "1933582     -5.871740e-10  \n",
       "1933583     -6.862273e-12  \n",
       "1933584      9.574263e-08  \n",
       "1933585     -6.615415e-08  \n",
       "\n",
       "[1933586 rows x 232 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_steps = [0, 0.1, 1, 20, 40, 60, 120, 180, 240, 300, 360, 480, 600, 720, 840, 960, 1080, 1200, 1500, 1800, 2400, 3000, 3600, 4500, 5400, 6300, 7200, 9000, 10800, 14400]\n",
    "\n",
    "df_rate = pd.DataFrame()\n",
    "# df_rate['Temperature'] = df['Temperature']\n",
    "    \n",
    "Species = ['dcSM', 'dcR', 'dcB', 'dcC', 'dcP', 'dcIMP1', 'dcIMP2', 'dcINT1']\n",
    "Conc = ['cSM', 'cR', 'cB', 'cC', 'cP', 'cIMP1', 'cIMP2', 'cINT1']\n",
    "\n",
    "for i in range(0, len(Species)):\n",
    "    species = Species[i]\n",
    "    conc = Conc[i]\n",
    "\n",
    "    df_rate[species+'_dt_0.1s'] = (x_df_raw[conc+'_0.1s']-x_df_raw[conc+'_0s'])/(time_steps[1]-time_steps[0])\n",
    "    df_rate[species+'_dt_1s'] = (x_df_raw[conc+'_1s']-x_df_raw[conc+'_0.1s'])/(time_steps[2]-time_steps[1])\n",
    "    df_rate[species+'_dt_20s'] = (x_df_raw[conc+'_20s']-x_df_raw[conc+'_1s'])/(time_steps[3]-time_steps[2])\n",
    "    df_rate[species+'_dt_40s'] = (x_df_raw[conc+'_40s']-x_df_raw[conc+'_20s'])/(time_steps[4]-time_steps[3])\n",
    "\n",
    "    df_rate[species+'_dt_60s'] = (x_df_raw[conc+'_60s']-x_df_raw[conc+'_40s'])/(time_steps[5]-time_steps[4])\n",
    "    df_rate[species+'_dt_120s'] = (x_df_raw[conc+'_120s']-x_df_raw[conc+'_60s'])/(time_steps[6]-time_steps[5])\n",
    "    df_rate[species+'_dt_180s'] = (x_df_raw[conc+'_180s']-x_df_raw[conc+'_120s'])/(time_steps[7]-time_steps[6])\n",
    "    df_rate[species+'_dt_240s'] = (x_df_raw[conc+'_240s']-x_df_raw[conc+'_180s'])/(time_steps[8]-time_steps[7])\n",
    "\n",
    "    df_rate[species+'_dt_300s'] = (x_df_raw[conc+'_300s']-x_df_raw[conc+'_240s'])/(time_steps[9]-time_steps[8])\n",
    "    df_rate[species+'_dt_360s'] = (x_df_raw[conc+'_360s']-x_df_raw[conc+'_300s'])/(time_steps[10]-time_steps[9])\n",
    "    df_rate[species+'_dt_480s'] = (x_df_raw[conc+'_480s']-x_df_raw[conc+'_360s'])/(time_steps[11]-time_steps[10])\n",
    "    df_rate[species+'_dt_600s'] = (x_df_raw[conc+'_600s']-x_df_raw[conc+'_480s'])/(time_steps[12]-time_steps[11])\n",
    "\n",
    "    df_rate[species+'_dt_720s'] = (x_df_raw[conc+'_720s']-x_df_raw[conc+'_600s'])/(time_steps[13]-time_steps[12])\n",
    "    df_rate[species+'_dt_840s'] = (x_df_raw[conc+'_840s']-x_df_raw[conc+'_720s'])/(time_steps[14]-time_steps[13])\n",
    "    df_rate[species+'_dt_960s'] = (x_df_raw[conc+'_960s']-x_df_raw[conc+'_840s'])/(time_steps[15]-time_steps[14])\n",
    "    df_rate[species+'_dt_1080s'] = (x_df_raw[conc+'_1080s']-x_df_raw[conc+'_960s'])/(time_steps[16]-time_steps[15])\n",
    "\n",
    "    df_rate[species+'_dt_1200s'] = (x_df_raw[conc+'_1200s']-x_df_raw[conc+'_1080s'])/(time_steps[17]-time_steps[16])\n",
    "    df_rate[species+'_dt_1500s'] = (x_df_raw[conc+'_1500s']-x_df_raw[conc+'_1200s'])/(time_steps[18]-time_steps[17])\n",
    "    df_rate[species+'_dt_1800s'] = (x_df_raw[conc+'_1800s']-x_df_raw[conc+'_1500s'])/(time_steps[19]-time_steps[18])\n",
    "    df_rate[species+'_dt_2400s'] = (x_df_raw[conc+'_2400s']-x_df_raw[conc+'_1800s'])/(time_steps[20]-time_steps[19])\n",
    "\n",
    "    df_rate[species+'_dt_3000s'] = (x_df_raw[conc+'_3000s']-x_df_raw[conc+'_2400s'])/(time_steps[21]-time_steps[20])\n",
    "    df_rate[species+'_dt_3600s'] = (x_df_raw[conc+'_3600s']-x_df_raw[conc+'_3000s'])/(time_steps[22]-time_steps[21])\n",
    "    df_rate[species+'_dt_4500s'] = (x_df_raw[conc+'_4500s']-x_df_raw[conc+'_3600s'])/(time_steps[23]-time_steps[22])\n",
    "    df_rate[species+'_dt_5400s'] = (x_df_raw[conc+'_5400s']-x_df_raw[conc+'_4500s'])/(time_steps[24]-time_steps[23])\n",
    "\n",
    "    df_rate[species+'_dt_6300s'] = (x_df_raw[conc+'_6300s']-x_df_raw[conc+'_5400s'])/(time_steps[25]-time_steps[24])\n",
    "    df_rate[species+'_dt_7200s'] = (x_df_raw[conc+'_7200s']-x_df_raw[conc+'_6300s'])/(time_steps[26]-time_steps[25])\n",
    "    df_rate[species+'_dt_9000s'] = (x_df_raw[conc+'_9000s']-x_df_raw[conc+'_7200s'])/(time_steps[27]-time_steps[26])\n",
    "    df_rate[species+'_dt_10800s'] = (x_df_raw[conc+'_10800s']-x_df_raw[conc+'_9000s'])/(time_steps[28]-time_steps[27])\n",
    "    df_rate[species+'_dt_14400s'] = (x_df_raw[conc+'_14400s']-x_df_raw[conc+'_10800s'])/(time_steps[29]-time_steps[28])\n",
    "\n",
    "df_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a MinMaxScaler instance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transpose the DataFrame so that rows become columns for scaling\n",
    "x_df_transposed = x_df_raw.T\n",
    "\n",
    "# Scale the entire transposed DataFrame\n",
    "scaled_data = scaler.fit_transform(x_df_transposed)\n",
    "\n",
    "# Transpose the scaled data back to the original orientation\n",
    "x = pd.DataFrame(scaled_data.T, columns=x_df_raw.columns, index=x_df_raw.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>cSM_360s</th>\n",
       "      <th>cSM_480s</th>\n",
       "      <th>cSM_600s</th>\n",
       "      <th>cSM_720s</th>\n",
       "      <th>cSM_840s</th>\n",
       "      <th>cSM_960s</th>\n",
       "      <th>cSM_1080s</th>\n",
       "      <th>cSM_1200s</th>\n",
       "      <th>cSM_1500s</th>\n",
       "      <th>cSM_1800s</th>\n",
       "      <th>cSM_2400s</th>\n",
       "      <th>cSM_3000s</th>\n",
       "      <th>cSM_3600s</th>\n",
       "      <th>cSM_4500s</th>\n",
       "      <th>cSM_5400s</th>\n",
       "      <th>cSM_6300s</th>\n",
       "      <th>cSM_7200s</th>\n",
       "      <th>cSM_9000s</th>\n",
       "      <th>cSM_10800s</th>\n",
       "      <th>cSM_14400s</th>\n",
       "      <th>cR_0s</th>\n",
       "      <th>cR_0.1s</th>\n",
       "      <th>cR_1s</th>\n",
       "      <th>cR_20s</th>\n",
       "      <th>cR_40s</th>\n",
       "      <th>cR_60s</th>\n",
       "      <th>cR_120s</th>\n",
       "      <th>cR_180s</th>\n",
       "      <th>cR_240s</th>\n",
       "      <th>cR_300s</th>\n",
       "      <th>cR_360s</th>\n",
       "      <th>cR_480s</th>\n",
       "      <th>cR_600s</th>\n",
       "      <th>cR_720s</th>\n",
       "      <th>cR_840s</th>\n",
       "      <th>cR_960s</th>\n",
       "      <th>cR_1080s</th>\n",
       "      <th>cR_1200s</th>\n",
       "      <th>cR_1500s</th>\n",
       "      <th>cR_1800s</th>\n",
       "      <th>cR_2400s</th>\n",
       "      <th>cR_3000s</th>\n",
       "      <th>cR_3600s</th>\n",
       "      <th>cR_4500s</th>\n",
       "      <th>cR_5400s</th>\n",
       "      <th>cR_6300s</th>\n",
       "      <th>cR_7200s</th>\n",
       "      <th>cR_9000s</th>\n",
       "      <th>cR_10800s</th>\n",
       "      <th>cR_14400s</th>\n",
       "      <th>cB_0s</th>\n",
       "      <th>cB_0.1s</th>\n",
       "      <th>cB_1s</th>\n",
       "      <th>cB_20s</th>\n",
       "      <th>cB_40s</th>\n",
       "      <th>cB_60s</th>\n",
       "      <th>cB_120s</th>\n",
       "      <th>cB_180s</th>\n",
       "      <th>cB_240s</th>\n",
       "      <th>cB_300s</th>\n",
       "      <th>cB_360s</th>\n",
       "      <th>cB_480s</th>\n",
       "      <th>cB_600s</th>\n",
       "      <th>cB_720s</th>\n",
       "      <th>cB_840s</th>\n",
       "      <th>cB_960s</th>\n",
       "      <th>cB_1080s</th>\n",
       "      <th>cB_1200s</th>\n",
       "      <th>cB_1500s</th>\n",
       "      <th>cB_1800s</th>\n",
       "      <th>cB_2400s</th>\n",
       "      <th>cB_3000s</th>\n",
       "      <th>cB_3600s</th>\n",
       "      <th>cB_4500s</th>\n",
       "      <th>cB_5400s</th>\n",
       "      <th>cB_6300s</th>\n",
       "      <th>cB_7200s</th>\n",
       "      <th>cB_9000s</th>\n",
       "      <th>cB_10800s</th>\n",
       "      <th>cB_14400s</th>\n",
       "      <th>cC_0s</th>\n",
       "      <th>cC_0.1s</th>\n",
       "      <th>cC_1s</th>\n",
       "      <th>cC_20s</th>\n",
       "      <th>cC_40s</th>\n",
       "      <th>cC_60s</th>\n",
       "      <th>cC_120s</th>\n",
       "      <th>cC_180s</th>\n",
       "      <th>cC_240s</th>\n",
       "      <th>cC_300s</th>\n",
       "      <th>cC_360s</th>\n",
       "      <th>cC_480s</th>\n",
       "      <th>cC_600s</th>\n",
       "      <th>cC_720s</th>\n",
       "      <th>cC_840s</th>\n",
       "      <th>cC_960s</th>\n",
       "      <th>cC_1080s</th>\n",
       "      <th>cC_1200s</th>\n",
       "      <th>cC_1500s</th>\n",
       "      <th>cC_1800s</th>\n",
       "      <th>cC_2400s</th>\n",
       "      <th>cC_3000s</th>\n",
       "      <th>cC_3600s</th>\n",
       "      <th>cC_4500s</th>\n",
       "      <th>cC_5400s</th>\n",
       "      <th>cC_6300s</th>\n",
       "      <th>cC_7200s</th>\n",
       "      <th>cC_9000s</th>\n",
       "      <th>cC_10800s</th>\n",
       "      <th>cC_14400s</th>\n",
       "      <th>cP_0s</th>\n",
       "      <th>cP_0.1s</th>\n",
       "      <th>cP_1s</th>\n",
       "      <th>cP_20s</th>\n",
       "      <th>cP_40s</th>\n",
       "      <th>cP_60s</th>\n",
       "      <th>cP_120s</th>\n",
       "      <th>cP_180s</th>\n",
       "      <th>cP_240s</th>\n",
       "      <th>cP_300s</th>\n",
       "      <th>cP_360s</th>\n",
       "      <th>cP_480s</th>\n",
       "      <th>cP_600s</th>\n",
       "      <th>cP_720s</th>\n",
       "      <th>cP_840s</th>\n",
       "      <th>cP_960s</th>\n",
       "      <th>cP_1080s</th>\n",
       "      <th>cP_1200s</th>\n",
       "      <th>cP_1500s</th>\n",
       "      <th>cP_1800s</th>\n",
       "      <th>cP_2400s</th>\n",
       "      <th>cP_3000s</th>\n",
       "      <th>cP_3600s</th>\n",
       "      <th>cP_4500s</th>\n",
       "      <th>cP_5400s</th>\n",
       "      <th>cP_6300s</th>\n",
       "      <th>cP_7200s</th>\n",
       "      <th>cP_9000s</th>\n",
       "      <th>cP_10800s</th>\n",
       "      <th>cP_14400s</th>\n",
       "      <th>cIMP1_0s</th>\n",
       "      <th>cIMP1_0.1s</th>\n",
       "      <th>cIMP1_1s</th>\n",
       "      <th>cIMP1_20s</th>\n",
       "      <th>cIMP1_40s</th>\n",
       "      <th>cIMP1_60s</th>\n",
       "      <th>cIMP1_120s</th>\n",
       "      <th>cIMP1_180s</th>\n",
       "      <th>cIMP1_240s</th>\n",
       "      <th>cIMP1_300s</th>\n",
       "      <th>cIMP1_360s</th>\n",
       "      <th>cIMP1_480s</th>\n",
       "      <th>cIMP1_600s</th>\n",
       "      <th>cIMP1_720s</th>\n",
       "      <th>cIMP1_840s</th>\n",
       "      <th>cIMP1_960s</th>\n",
       "      <th>cIMP1_1080s</th>\n",
       "      <th>cIMP1_1200s</th>\n",
       "      <th>cIMP1_1500s</th>\n",
       "      <th>cIMP1_1800s</th>\n",
       "      <th>cIMP1_2400s</th>\n",
       "      <th>cIMP1_3000s</th>\n",
       "      <th>cIMP1_3600s</th>\n",
       "      <th>cIMP1_4500s</th>\n",
       "      <th>cIMP1_5400s</th>\n",
       "      <th>cIMP1_6300s</th>\n",
       "      <th>cIMP1_7200s</th>\n",
       "      <th>cIMP1_9000s</th>\n",
       "      <th>cIMP1_10800s</th>\n",
       "      <th>cIMP1_14400s</th>\n",
       "      <th>cIMP2_0s</th>\n",
       "      <th>cIMP2_0.1s</th>\n",
       "      <th>cIMP2_1s</th>\n",
       "      <th>cIMP2_20s</th>\n",
       "      <th>cIMP2_40s</th>\n",
       "      <th>cIMP2_60s</th>\n",
       "      <th>cIMP2_120s</th>\n",
       "      <th>cIMP2_180s</th>\n",
       "      <th>cIMP2_240s</th>\n",
       "      <th>cIMP2_300s</th>\n",
       "      <th>cIMP2_360s</th>\n",
       "      <th>cIMP2_480s</th>\n",
       "      <th>cIMP2_600s</th>\n",
       "      <th>cIMP2_720s</th>\n",
       "      <th>cIMP2_840s</th>\n",
       "      <th>cIMP2_960s</th>\n",
       "      <th>cIMP2_1080s</th>\n",
       "      <th>cIMP2_1200s</th>\n",
       "      <th>cIMP2_1500s</th>\n",
       "      <th>cIMP2_1800s</th>\n",
       "      <th>cIMP2_2400s</th>\n",
       "      <th>cIMP2_3000s</th>\n",
       "      <th>cIMP2_3600s</th>\n",
       "      <th>cIMP2_4500s</th>\n",
       "      <th>cIMP2_5400s</th>\n",
       "      <th>cIMP2_6300s</th>\n",
       "      <th>cIMP2_7200s</th>\n",
       "      <th>cIMP2_9000s</th>\n",
       "      <th>cIMP2_10800s</th>\n",
       "      <th>cIMP2_14400s</th>\n",
       "      <th>cINT1_0s</th>\n",
       "      <th>cINT1_0.1s</th>\n",
       "      <th>cINT1_1s</th>\n",
       "      <th>cINT1_20s</th>\n",
       "      <th>cINT1_40s</th>\n",
       "      <th>cINT1_60s</th>\n",
       "      <th>cINT1_120s</th>\n",
       "      <th>cINT1_180s</th>\n",
       "      <th>cINT1_240s</th>\n",
       "      <th>cINT1_300s</th>\n",
       "      <th>cINT1_360s</th>\n",
       "      <th>cINT1_480s</th>\n",
       "      <th>cINT1_600s</th>\n",
       "      <th>cINT1_720s</th>\n",
       "      <th>cINT1_840s</th>\n",
       "      <th>cINT1_960s</th>\n",
       "      <th>cINT1_1080s</th>\n",
       "      <th>cINT1_1200s</th>\n",
       "      <th>cINT1_1500s</th>\n",
       "      <th>cINT1_1800s</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333324</td>\n",
       "      <td>0.333152</td>\n",
       "      <td>0.332972</td>\n",
       "      <td>0.332793</td>\n",
       "      <td>0.332261</td>\n",
       "      <td>0.331738</td>\n",
       "      <td>0.331224</td>\n",
       "      <td>0.330717</td>\n",
       "      <td>0.330219</td>\n",
       "      <td>0.329245</td>\n",
       "      <td>0.328301</td>\n",
       "      <td>0.327383</td>\n",
       "      <td>0.326491</td>\n",
       "      <td>0.325624</td>\n",
       "      <td>0.324782</td>\n",
       "      <td>0.323963</td>\n",
       "      <td>0.322011</td>\n",
       "      <td>0.320173</td>\n",
       "      <td>0.316805</td>\n",
       "      <td>0.313784</td>\n",
       "      <td>0.311040</td>\n",
       "      <td>0.307362</td>\n",
       "      <td>0.304090</td>\n",
       "      <td>0.301175</td>\n",
       "      <td>0.298505</td>\n",
       "      <td>0.293870</td>\n",
       "      <td>0.289895</td>\n",
       "      <td>0.283418</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.499999</td>\n",
       "      <td>0.499991</td>\n",
       "      <td>0.499819</td>\n",
       "      <td>0.499639</td>\n",
       "      <td>0.499459</td>\n",
       "      <td>0.498927</td>\n",
       "      <td>0.498404</td>\n",
       "      <td>0.497887</td>\n",
       "      <td>0.497378</td>\n",
       "      <td>0.496877</td>\n",
       "      <td>0.495894</td>\n",
       "      <td>0.494934</td>\n",
       "      <td>0.493990</td>\n",
       "      <td>0.493066</td>\n",
       "      <td>0.492160</td>\n",
       "      <td>0.491270</td>\n",
       "      <td>0.490397</td>\n",
       "      <td>0.488275</td>\n",
       "      <td>0.486205</td>\n",
       "      <td>0.482223</td>\n",
       "      <td>0.478432</td>\n",
       "      <td>0.474801</td>\n",
       "      <td>0.469672</td>\n",
       "      <td>0.464911</td>\n",
       "      <td>0.460496</td>\n",
       "      <td>0.456415</td>\n",
       "      <td>0.449129</td>\n",
       "      <td>0.442812</td>\n",
       "      <td>0.432417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999995</td>\n",
       "      <td>0.999909</td>\n",
       "      <td>0.999819</td>\n",
       "      <td>0.999730</td>\n",
       "      <td>0.999464</td>\n",
       "      <td>0.999202</td>\n",
       "      <td>0.998944</td>\n",
       "      <td>0.998689</td>\n",
       "      <td>0.998438</td>\n",
       "      <td>0.997947</td>\n",
       "      <td>0.997467</td>\n",
       "      <td>0.996995</td>\n",
       "      <td>0.996533</td>\n",
       "      <td>0.996080</td>\n",
       "      <td>0.995635</td>\n",
       "      <td>0.995198</td>\n",
       "      <td>0.994137</td>\n",
       "      <td>0.993103</td>\n",
       "      <td>0.991112</td>\n",
       "      <td>0.989216</td>\n",
       "      <td>0.987401</td>\n",
       "      <td>0.984836</td>\n",
       "      <td>0.982456</td>\n",
       "      <td>0.980248</td>\n",
       "      <td>0.978207</td>\n",
       "      <td>0.974564</td>\n",
       "      <td>0.971406</td>\n",
       "      <td>0.966208</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.099999</td>\n",
       "      <td>0.099991</td>\n",
       "      <td>0.099819</td>\n",
       "      <td>0.099639</td>\n",
       "      <td>0.099459</td>\n",
       "      <td>0.098928</td>\n",
       "      <td>0.098405</td>\n",
       "      <td>0.097892</td>\n",
       "      <td>0.097386</td>\n",
       "      <td>0.096890</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.094985</td>\n",
       "      <td>0.094079</td>\n",
       "      <td>0.093203</td>\n",
       "      <td>0.092356</td>\n",
       "      <td>0.091538</td>\n",
       "      <td>0.090746</td>\n",
       "      <td>0.088880</td>\n",
       "      <td>0.087157</td>\n",
       "      <td>0.084096</td>\n",
       "      <td>0.081459</td>\n",
       "      <td>0.079159</td>\n",
       "      <td>0.076207</td>\n",
       "      <td>0.073680</td>\n",
       "      <td>0.071514</td>\n",
       "      <td>0.069551</td>\n",
       "      <td>0.066241</td>\n",
       "      <td>0.063437</td>\n",
       "      <td>0.058919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.542286e-07</td>\n",
       "      <td>4.541710e-06</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.001055</td>\n",
       "      <td>0.001308</td>\n",
       "      <td>0.001557</td>\n",
       "      <td>0.002044</td>\n",
       "      <td>0.002516</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>0.003421</td>\n",
       "      <td>0.003855</td>\n",
       "      <td>0.004276</td>\n",
       "      <td>0.004685</td>\n",
       "      <td>0.005661</td>\n",
       "      <td>0.006580</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.009775</td>\n",
       "      <td>0.011147</td>\n",
       "      <td>0.012986</td>\n",
       "      <td>0.014621</td>\n",
       "      <td>0.016079</td>\n",
       "      <td>0.017414</td>\n",
       "      <td>0.019731</td>\n",
       "      <td>0.021719</td>\n",
       "      <td>0.024957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.967202e-17</td>\n",
       "      <td>8.989862e-14</td>\n",
       "      <td>7.744688e-10</td>\n",
       "      <td>6.273261e-09</td>\n",
       "      <td>2.012814e-08</td>\n",
       "      <td>1.808198e-07</td>\n",
       "      <td>6.460319e-07</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.001452</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.002923</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.004379</td>\n",
       "      <td>0.005704</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>0.008834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.542286e-07</td>\n",
       "      <td>4.541710e-06</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000536</td>\n",
       "      <td>0.000796</td>\n",
       "      <td>0.001052</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.002026</td>\n",
       "      <td>0.002483</td>\n",
       "      <td>0.002916</td>\n",
       "      <td>0.003330</td>\n",
       "      <td>0.003723</td>\n",
       "      <td>0.004097</td>\n",
       "      <td>0.004452</td>\n",
       "      <td>0.005258</td>\n",
       "      <td>0.005946</td>\n",
       "      <td>0.007016</td>\n",
       "      <td>0.007757</td>\n",
       "      <td>0.008242</td>\n",
       "      <td>0.008630</td>\n",
       "      <td>0.008775</td>\n",
       "      <td>0.008734</td>\n",
       "      <td>0.008657</td>\n",
       "      <td>0.008323</td>\n",
       "      <td>0.007969</td>\n",
       "      <td>0.007289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333309</td>\n",
       "      <td>0.333285</td>\n",
       "      <td>0.333261</td>\n",
       "      <td>0.333189</td>\n",
       "      <td>0.333117</td>\n",
       "      <td>0.333045</td>\n",
       "      <td>0.332973</td>\n",
       "      <td>0.332901</td>\n",
       "      <td>0.332758</td>\n",
       "      <td>0.332616</td>\n",
       "      <td>0.332475</td>\n",
       "      <td>0.332334</td>\n",
       "      <td>0.332193</td>\n",
       "      <td>0.332053</td>\n",
       "      <td>0.331914</td>\n",
       "      <td>0.331569</td>\n",
       "      <td>0.331227</td>\n",
       "      <td>0.330552</td>\n",
       "      <td>0.329891</td>\n",
       "      <td>0.329242</td>\n",
       "      <td>0.328292</td>\n",
       "      <td>0.327366</td>\n",
       "      <td>0.326465</td>\n",
       "      <td>0.325588</td>\n",
       "      <td>0.323900</td>\n",
       "      <td>0.322285</td>\n",
       "      <td>0.319298</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499999</td>\n",
       "      <td>0.499976</td>\n",
       "      <td>0.499952</td>\n",
       "      <td>0.499928</td>\n",
       "      <td>0.499855</td>\n",
       "      <td>0.499781</td>\n",
       "      <td>0.499707</td>\n",
       "      <td>0.499632</td>\n",
       "      <td>0.499556</td>\n",
       "      <td>0.499402</td>\n",
       "      <td>0.499242</td>\n",
       "      <td>0.499074</td>\n",
       "      <td>0.498900</td>\n",
       "      <td>0.498722</td>\n",
       "      <td>0.498540</td>\n",
       "      <td>0.498354</td>\n",
       "      <td>0.497878</td>\n",
       "      <td>0.497388</td>\n",
       "      <td>0.496388</td>\n",
       "      <td>0.495405</td>\n",
       "      <td>0.494429</td>\n",
       "      <td>0.492991</td>\n",
       "      <td>0.491599</td>\n",
       "      <td>0.490243</td>\n",
       "      <td>0.488922</td>\n",
       "      <td>0.486377</td>\n",
       "      <td>0.483945</td>\n",
       "      <td>0.479447</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999988</td>\n",
       "      <td>0.999976</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>0.999927</td>\n",
       "      <td>0.999891</td>\n",
       "      <td>0.999854</td>\n",
       "      <td>0.999816</td>\n",
       "      <td>0.999778</td>\n",
       "      <td>0.999701</td>\n",
       "      <td>0.999621</td>\n",
       "      <td>0.999537</td>\n",
       "      <td>0.999450</td>\n",
       "      <td>0.999361</td>\n",
       "      <td>0.999270</td>\n",
       "      <td>0.999177</td>\n",
       "      <td>0.998939</td>\n",
       "      <td>0.998694</td>\n",
       "      <td>0.998194</td>\n",
       "      <td>0.997703</td>\n",
       "      <td>0.997215</td>\n",
       "      <td>0.996495</td>\n",
       "      <td>0.995799</td>\n",
       "      <td>0.995121</td>\n",
       "      <td>0.994461</td>\n",
       "      <td>0.993189</td>\n",
       "      <td>0.991972</td>\n",
       "      <td>0.989723</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.099999</td>\n",
       "      <td>0.099976</td>\n",
       "      <td>0.099952</td>\n",
       "      <td>0.099928</td>\n",
       "      <td>0.099856</td>\n",
       "      <td>0.099784</td>\n",
       "      <td>0.099713</td>\n",
       "      <td>0.099643</td>\n",
       "      <td>0.099574</td>\n",
       "      <td>0.099437</td>\n",
       "      <td>0.099303</td>\n",
       "      <td>0.099175</td>\n",
       "      <td>0.099050</td>\n",
       "      <td>0.098929</td>\n",
       "      <td>0.098810</td>\n",
       "      <td>0.098694</td>\n",
       "      <td>0.098414</td>\n",
       "      <td>0.098146</td>\n",
       "      <td>0.097635</td>\n",
       "      <td>0.097134</td>\n",
       "      <td>0.096649</td>\n",
       "      <td>0.095942</td>\n",
       "      <td>0.095250</td>\n",
       "      <td>0.094577</td>\n",
       "      <td>0.093922</td>\n",
       "      <td>0.092662</td>\n",
       "      <td>0.091455</td>\n",
       "      <td>0.089223</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.043392e-08</td>\n",
       "      <td>6.043290e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000429</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>0.001390</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.002046</td>\n",
       "      <td>0.002521</td>\n",
       "      <td>0.002984</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.004717</td>\n",
       "      <td>0.005524</td>\n",
       "      <td>0.007018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.087332e-16</td>\n",
       "      <td>1.087824e-13</td>\n",
       "      <td>9.560027e-10</td>\n",
       "      <td>7.810140e-09</td>\n",
       "      <td>2.497200e-08</td>\n",
       "      <td>2.488103e-07</td>\n",
       "      <td>9.247170e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>0.000740</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>0.001445</td>\n",
       "      <td>0.001667</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.002503</td>\n",
       "      <td>0.003259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.043392e-08</td>\n",
       "      <td>6.043288e-07</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>0.000548</td>\n",
       "      <td>0.000559</td>\n",
       "      <td>0.000569</td>\n",
       "      <td>0.000566</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333329</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.333170</td>\n",
       "      <td>0.333089</td>\n",
       "      <td>0.332846</td>\n",
       "      <td>0.332605</td>\n",
       "      <td>0.332365</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>0.331891</td>\n",
       "      <td>0.331423</td>\n",
       "      <td>0.330961</td>\n",
       "      <td>0.330506</td>\n",
       "      <td>0.330056</td>\n",
       "      <td>0.329612</td>\n",
       "      <td>0.329173</td>\n",
       "      <td>0.328740</td>\n",
       "      <td>0.327680</td>\n",
       "      <td>0.326652</td>\n",
       "      <td>0.324688</td>\n",
       "      <td>0.322837</td>\n",
       "      <td>0.321075</td>\n",
       "      <td>0.318594</td>\n",
       "      <td>0.316298</td>\n",
       "      <td>0.314168</td>\n",
       "      <td>0.312169</td>\n",
       "      <td>0.308500</td>\n",
       "      <td>0.305247</td>\n",
       "      <td>0.299642</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499996</td>\n",
       "      <td>0.499917</td>\n",
       "      <td>0.499826</td>\n",
       "      <td>0.499728</td>\n",
       "      <td>0.499403</td>\n",
       "      <td>0.499051</td>\n",
       "      <td>0.498696</td>\n",
       "      <td>0.498337</td>\n",
       "      <td>0.497980</td>\n",
       "      <td>0.497278</td>\n",
       "      <td>0.496586</td>\n",
       "      <td>0.495901</td>\n",
       "      <td>0.495225</td>\n",
       "      <td>0.494558</td>\n",
       "      <td>0.493899</td>\n",
       "      <td>0.493248</td>\n",
       "      <td>0.491657</td>\n",
       "      <td>0.490113</td>\n",
       "      <td>0.487164</td>\n",
       "      <td>0.484385</td>\n",
       "      <td>0.481740</td>\n",
       "      <td>0.478014</td>\n",
       "      <td>0.474567</td>\n",
       "      <td>0.471369</td>\n",
       "      <td>0.468368</td>\n",
       "      <td>0.462860</td>\n",
       "      <td>0.457976</td>\n",
       "      <td>0.449561</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999958</td>\n",
       "      <td>0.999913</td>\n",
       "      <td>0.999864</td>\n",
       "      <td>0.999702</td>\n",
       "      <td>0.999525</td>\n",
       "      <td>0.999348</td>\n",
       "      <td>0.999169</td>\n",
       "      <td>0.998990</td>\n",
       "      <td>0.998639</td>\n",
       "      <td>0.998293</td>\n",
       "      <td>0.997951</td>\n",
       "      <td>0.997612</td>\n",
       "      <td>0.997279</td>\n",
       "      <td>0.996949</td>\n",
       "      <td>0.996624</td>\n",
       "      <td>0.995828</td>\n",
       "      <td>0.995057</td>\n",
       "      <td>0.993582</td>\n",
       "      <td>0.992192</td>\n",
       "      <td>0.990870</td>\n",
       "      <td>0.989007</td>\n",
       "      <td>0.987284</td>\n",
       "      <td>0.985684</td>\n",
       "      <td>0.984184</td>\n",
       "      <td>0.981430</td>\n",
       "      <td>0.978988</td>\n",
       "      <td>0.974781</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.099996</td>\n",
       "      <td>0.099919</td>\n",
       "      <td>0.099842</td>\n",
       "      <td>0.099769</td>\n",
       "      <td>0.099567</td>\n",
       "      <td>0.099381</td>\n",
       "      <td>0.099199</td>\n",
       "      <td>0.099022</td>\n",
       "      <td>0.098846</td>\n",
       "      <td>0.098495</td>\n",
       "      <td>0.098149</td>\n",
       "      <td>0.097808</td>\n",
       "      <td>0.097472</td>\n",
       "      <td>0.097139</td>\n",
       "      <td>0.096810</td>\n",
       "      <td>0.096486</td>\n",
       "      <td>0.095691</td>\n",
       "      <td>0.094921</td>\n",
       "      <td>0.093449</td>\n",
       "      <td>0.092063</td>\n",
       "      <td>0.090743</td>\n",
       "      <td>0.088883</td>\n",
       "      <td>0.087163</td>\n",
       "      <td>0.085567</td>\n",
       "      <td>0.084069</td>\n",
       "      <td>0.081320</td>\n",
       "      <td>0.078883</td>\n",
       "      <td>0.074683</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.046509e-07</td>\n",
       "      <td>2.046392e-06</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>0.000244</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000484</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>0.000955</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>0.001414</td>\n",
       "      <td>0.001639</td>\n",
       "      <td>0.001861</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.002297</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>0.003341</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.005248</td>\n",
       "      <td>0.006129</td>\n",
       "      <td>0.007370</td>\n",
       "      <td>0.008518</td>\n",
       "      <td>0.009583</td>\n",
       "      <td>0.010582</td>\n",
       "      <td>0.012417</td>\n",
       "      <td>0.014043</td>\n",
       "      <td>0.016845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.766025e-14</td>\n",
       "      <td>6.912635e-11</td>\n",
       "      <td>7.050067e-07</td>\n",
       "      <td>5.285923e-06</td>\n",
       "      <td>1.346008e-05</td>\n",
       "      <td>5.464402e-05</td>\n",
       "      <td>1.101606e-04</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000521</td>\n",
       "      <td>0.000636</td>\n",
       "      <td>0.000749</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.001079</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>0.001603</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.002559</td>\n",
       "      <td>0.003001</td>\n",
       "      <td>0.003623</td>\n",
       "      <td>0.004199</td>\n",
       "      <td>0.004733</td>\n",
       "      <td>0.005234</td>\n",
       "      <td>0.006153</td>\n",
       "      <td>0.006969</td>\n",
       "      <td>0.008374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.046508e-07</td>\n",
       "      <td>2.046254e-06</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000095</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0.000115</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333321</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.330901</td>\n",
       "      <td>0.328639</td>\n",
       "      <td>0.326530</td>\n",
       "      <td>0.320964</td>\n",
       "      <td>0.316285</td>\n",
       "      <td>0.312280</td>\n",
       "      <td>0.308774</td>\n",
       "      <td>0.305711</td>\n",
       "      <td>0.300441</td>\n",
       "      <td>0.296122</td>\n",
       "      <td>0.292450</td>\n",
       "      <td>0.289299</td>\n",
       "      <td>0.286503</td>\n",
       "      <td>0.284045</td>\n",
       "      <td>0.281825</td>\n",
       "      <td>0.277100</td>\n",
       "      <td>0.273229</td>\n",
       "      <td>0.267176</td>\n",
       "      <td>0.262549</td>\n",
       "      <td>0.258777</td>\n",
       "      <td>0.254275</td>\n",
       "      <td>0.250687</td>\n",
       "      <td>0.247689</td>\n",
       "      <td>0.245179</td>\n",
       "      <td>0.241075</td>\n",
       "      <td>0.237877</td>\n",
       "      <td>0.233093</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.499987</td>\n",
       "      <td>0.499874</td>\n",
       "      <td>0.497568</td>\n",
       "      <td>0.495303</td>\n",
       "      <td>0.493191</td>\n",
       "      <td>0.487592</td>\n",
       "      <td>0.482843</td>\n",
       "      <td>0.478725</td>\n",
       "      <td>0.475064</td>\n",
       "      <td>0.471807</td>\n",
       "      <td>0.466040</td>\n",
       "      <td>0.461111</td>\n",
       "      <td>0.456740</td>\n",
       "      <td>0.452827</td>\n",
       "      <td>0.449230</td>\n",
       "      <td>0.445941</td>\n",
       "      <td>0.442878</td>\n",
       "      <td>0.436045</td>\n",
       "      <td>0.430131</td>\n",
       "      <td>0.420319</td>\n",
       "      <td>0.412421</td>\n",
       "      <td>0.405823</td>\n",
       "      <td>0.397754</td>\n",
       "      <td>0.391236</td>\n",
       "      <td>0.385763</td>\n",
       "      <td>0.381158</td>\n",
       "      <td>0.373627</td>\n",
       "      <td>0.367760</td>\n",
       "      <td>0.359012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>0.998784</td>\n",
       "      <td>0.997652</td>\n",
       "      <td>0.996595</td>\n",
       "      <td>0.993796</td>\n",
       "      <td>0.991422</td>\n",
       "      <td>0.989363</td>\n",
       "      <td>0.987532</td>\n",
       "      <td>0.985904</td>\n",
       "      <td>0.983020</td>\n",
       "      <td>0.980555</td>\n",
       "      <td>0.978370</td>\n",
       "      <td>0.976414</td>\n",
       "      <td>0.974615</td>\n",
       "      <td>0.972970</td>\n",
       "      <td>0.971439</td>\n",
       "      <td>0.968022</td>\n",
       "      <td>0.965066</td>\n",
       "      <td>0.960159</td>\n",
       "      <td>0.956211</td>\n",
       "      <td>0.952912</td>\n",
       "      <td>0.948877</td>\n",
       "      <td>0.945618</td>\n",
       "      <td>0.942882</td>\n",
       "      <td>0.940579</td>\n",
       "      <td>0.936814</td>\n",
       "      <td>0.933880</td>\n",
       "      <td>0.929506</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.099987</td>\n",
       "      <td>0.099874</td>\n",
       "      <td>0.097568</td>\n",
       "      <td>0.095306</td>\n",
       "      <td>0.093200</td>\n",
       "      <td>0.087650</td>\n",
       "      <td>0.083007</td>\n",
       "      <td>0.079057</td>\n",
       "      <td>0.075629</td>\n",
       "      <td>0.072664</td>\n",
       "      <td>0.067641</td>\n",
       "      <td>0.063628</td>\n",
       "      <td>0.060306</td>\n",
       "      <td>0.057535</td>\n",
       "      <td>0.055139</td>\n",
       "      <td>0.053097</td>\n",
       "      <td>0.051298</td>\n",
       "      <td>0.047628</td>\n",
       "      <td>0.044778</td>\n",
       "      <td>0.040604</td>\n",
       "      <td>0.037613</td>\n",
       "      <td>0.035254</td>\n",
       "      <td>0.032535</td>\n",
       "      <td>0.030413</td>\n",
       "      <td>0.028652</td>\n",
       "      <td>0.027189</td>\n",
       "      <td>0.024798</td>\n",
       "      <td>0.022935</td>\n",
       "      <td>0.020134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.313183e-06</td>\n",
       "      <td>6.302088e-05</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002347</td>\n",
       "      <td>0.003402</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>0.008524</td>\n",
       "      <td>0.010527</td>\n",
       "      <td>0.012280</td>\n",
       "      <td>0.013811</td>\n",
       "      <td>0.016446</td>\n",
       "      <td>0.018605</td>\n",
       "      <td>0.020441</td>\n",
       "      <td>0.022017</td>\n",
       "      <td>0.023415</td>\n",
       "      <td>0.024644</td>\n",
       "      <td>0.025754</td>\n",
       "      <td>0.028117</td>\n",
       "      <td>0.030052</td>\n",
       "      <td>0.033079</td>\n",
       "      <td>0.035392</td>\n",
       "      <td>0.037278</td>\n",
       "      <td>0.039529</td>\n",
       "      <td>0.041323</td>\n",
       "      <td>0.042822</td>\n",
       "      <td>0.044077</td>\n",
       "      <td>0.046129</td>\n",
       "      <td>0.047728</td>\n",
       "      <td>0.050120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.572048e-14</td>\n",
       "      <td>1.623255e-11</td>\n",
       "      <td>1.396851e-07</td>\n",
       "      <td>1.072748e-06</td>\n",
       "      <td>3.086097e-06</td>\n",
       "      <td>1.917127e-05</td>\n",
       "      <td>5.449461e-05</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>0.001970</td>\n",
       "      <td>0.002385</td>\n",
       "      <td>0.002807</td>\n",
       "      <td>0.003861</td>\n",
       "      <td>0.004882</td>\n",
       "      <td>0.006762</td>\n",
       "      <td>0.008397</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>0.011594</td>\n",
       "      <td>0.013059</td>\n",
       "      <td>0.014296</td>\n",
       "      <td>0.015344</td>\n",
       "      <td>0.017057</td>\n",
       "      <td>0.018392</td>\n",
       "      <td>0.020374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.313183e-06</td>\n",
       "      <td>6.302085e-05</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.002345</td>\n",
       "      <td>0.003395</td>\n",
       "      <td>0.006146</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>0.010305</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>0.013240</td>\n",
       "      <td>0.015379</td>\n",
       "      <td>0.016927</td>\n",
       "      <td>0.018065</td>\n",
       "      <td>0.018878</td>\n",
       "      <td>0.019476</td>\n",
       "      <td>0.019873</td>\n",
       "      <td>0.020141</td>\n",
       "      <td>0.020394</td>\n",
       "      <td>0.020287</td>\n",
       "      <td>0.019555</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.017658</td>\n",
       "      <td>0.016342</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.014230</td>\n",
       "      <td>0.013390</td>\n",
       "      <td>0.012016</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.009372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.322389</td>\n",
       "      <td>0.314400</td>\n",
       "      <td>0.308178</td>\n",
       "      <td>0.295191</td>\n",
       "      <td>0.286609</td>\n",
       "      <td>0.280245</td>\n",
       "      <td>0.275239</td>\n",
       "      <td>0.271128</td>\n",
       "      <td>0.264667</td>\n",
       "      <td>0.259731</td>\n",
       "      <td>0.255802</td>\n",
       "      <td>0.252532</td>\n",
       "      <td>0.249784</td>\n",
       "      <td>0.247392</td>\n",
       "      <td>0.245327</td>\n",
       "      <td>0.241058</td>\n",
       "      <td>0.237746</td>\n",
       "      <td>0.232842</td>\n",
       "      <td>0.229331</td>\n",
       "      <td>0.226625</td>\n",
       "      <td>0.223549</td>\n",
       "      <td>0.221242</td>\n",
       "      <td>0.219386</td>\n",
       "      <td>0.217909</td>\n",
       "      <td>0.215574</td>\n",
       "      <td>0.213859</td>\n",
       "      <td>0.211448</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.499935</td>\n",
       "      <td>0.499353</td>\n",
       "      <td>0.488959</td>\n",
       "      <td>0.480514</td>\n",
       "      <td>0.473451</td>\n",
       "      <td>0.456658</td>\n",
       "      <td>0.443903</td>\n",
       "      <td>0.433784</td>\n",
       "      <td>0.425542</td>\n",
       "      <td>0.418673</td>\n",
       "      <td>0.407761</td>\n",
       "      <td>0.399388</td>\n",
       "      <td>0.392717</td>\n",
       "      <td>0.387176</td>\n",
       "      <td>0.382523</td>\n",
       "      <td>0.378481</td>\n",
       "      <td>0.374997</td>\n",
       "      <td>0.367811</td>\n",
       "      <td>0.362254</td>\n",
       "      <td>0.354053</td>\n",
       "      <td>0.348201</td>\n",
       "      <td>0.343704</td>\n",
       "      <td>0.338604</td>\n",
       "      <td>0.334787</td>\n",
       "      <td>0.331723</td>\n",
       "      <td>0.329288</td>\n",
       "      <td>0.325444</td>\n",
       "      <td>0.322626</td>\n",
       "      <td>0.318671</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999967</td>\n",
       "      <td>0.999676</td>\n",
       "      <td>0.994480</td>\n",
       "      <td>0.990257</td>\n",
       "      <td>0.986725</td>\n",
       "      <td>0.978329</td>\n",
       "      <td>0.971952</td>\n",
       "      <td>0.966892</td>\n",
       "      <td>0.962771</td>\n",
       "      <td>0.959336</td>\n",
       "      <td>0.953881</td>\n",
       "      <td>0.949694</td>\n",
       "      <td>0.946359</td>\n",
       "      <td>0.943588</td>\n",
       "      <td>0.941262</td>\n",
       "      <td>0.939240</td>\n",
       "      <td>0.937499</td>\n",
       "      <td>0.933905</td>\n",
       "      <td>0.931127</td>\n",
       "      <td>0.927026</td>\n",
       "      <td>0.924100</td>\n",
       "      <td>0.921852</td>\n",
       "      <td>0.919302</td>\n",
       "      <td>0.917394</td>\n",
       "      <td>0.915861</td>\n",
       "      <td>0.914644</td>\n",
       "      <td>0.912722</td>\n",
       "      <td>0.911313</td>\n",
       "      <td>0.909335</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.099935</td>\n",
       "      <td>0.099353</td>\n",
       "      <td>0.089104</td>\n",
       "      <td>0.081342</td>\n",
       "      <td>0.075542</td>\n",
       "      <td>0.064458</td>\n",
       "      <td>0.057962</td>\n",
       "      <td>0.053476</td>\n",
       "      <td>0.050087</td>\n",
       "      <td>0.047356</td>\n",
       "      <td>0.043119</td>\n",
       "      <td>0.039903</td>\n",
       "      <td>0.037344</td>\n",
       "      <td>0.035211</td>\n",
       "      <td>0.033414</td>\n",
       "      <td>0.031847</td>\n",
       "      <td>0.030492</td>\n",
       "      <td>0.027681</td>\n",
       "      <td>0.025493</td>\n",
       "      <td>0.022237</td>\n",
       "      <td>0.019895</td>\n",
       "      <td>0.018086</td>\n",
       "      <td>0.016022</td>\n",
       "      <td>0.014469</td>\n",
       "      <td>0.013218</td>\n",
       "      <td>0.012220</td>\n",
       "      <td>0.010640</td>\n",
       "      <td>0.009476</td>\n",
       "      <td>0.007837</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.266661e-05</td>\n",
       "      <td>3.237267e-04</td>\n",
       "      <td>0.005472</td>\n",
       "      <td>0.009467</td>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.019071</td>\n",
       "      <td>0.023362</td>\n",
       "      <td>0.026544</td>\n",
       "      <td>0.029047</td>\n",
       "      <td>0.031102</td>\n",
       "      <td>0.034333</td>\n",
       "      <td>0.036801</td>\n",
       "      <td>0.038766</td>\n",
       "      <td>0.040400</td>\n",
       "      <td>0.041775</td>\n",
       "      <td>0.042971</td>\n",
       "      <td>0.044003</td>\n",
       "      <td>0.046138</td>\n",
       "      <td>0.047793</td>\n",
       "      <td>0.050245</td>\n",
       "      <td>0.052001</td>\n",
       "      <td>0.053354</td>\n",
       "      <td>0.054892</td>\n",
       "      <td>0.056046</td>\n",
       "      <td>0.056974</td>\n",
       "      <td>0.057712</td>\n",
       "      <td>0.058880</td>\n",
       "      <td>0.059737</td>\n",
       "      <td>0.060942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.461659e-12</td>\n",
       "      <td>9.576828e-09</td>\n",
       "      <td>4.840517e-05</td>\n",
       "      <td>2.760846e-04</td>\n",
       "      <td>6.969441e-04</td>\n",
       "      <td>2.600025e-03</td>\n",
       "      <td>4.686422e-03</td>\n",
       "      <td>0.006564</td>\n",
       "      <td>0.008181</td>\n",
       "      <td>0.009561</td>\n",
       "      <td>0.011786</td>\n",
       "      <td>0.013505</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>0.016011</td>\n",
       "      <td>0.016964</td>\n",
       "      <td>0.017789</td>\n",
       "      <td>0.018498</td>\n",
       "      <td>0.019957</td>\n",
       "      <td>0.021079</td>\n",
       "      <td>0.022728</td>\n",
       "      <td>0.023898</td>\n",
       "      <td>0.024794</td>\n",
       "      <td>0.025806</td>\n",
       "      <td>0.026561</td>\n",
       "      <td>0.027165</td>\n",
       "      <td>0.027644</td>\n",
       "      <td>0.028399</td>\n",
       "      <td>0.028950</td>\n",
       "      <td>0.029722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.266660e-05</td>\n",
       "      <td>3.237075e-04</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.008915</td>\n",
       "      <td>0.011184</td>\n",
       "      <td>0.013871</td>\n",
       "      <td>0.013989</td>\n",
       "      <td>0.013416</td>\n",
       "      <td>0.012685</td>\n",
       "      <td>0.011980</td>\n",
       "      <td>0.010762</td>\n",
       "      <td>0.009791</td>\n",
       "      <td>0.009015</td>\n",
       "      <td>0.008378</td>\n",
       "      <td>0.007848</td>\n",
       "      <td>0.007393</td>\n",
       "      <td>0.007007</td>\n",
       "      <td>0.006224</td>\n",
       "      <td>0.005635</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004205</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.002643</td>\n",
       "      <td>0.002424</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.001837</td>\n",
       "      <td>0.001498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "0  0.333333  0.333332  0.333324  0.333152  0.332972  0.332793  0.332261   \n",
       "1  0.333333  0.333333  0.333332  0.333309  0.333285  0.333261  0.333189   \n",
       "2  0.333333  0.333333  0.333329  0.333252  0.333170  0.333089  0.332846   \n",
       "3  0.333333  0.333321  0.333207  0.330901  0.328639  0.326530  0.320964   \n",
       "4  0.333333  0.333268  0.332686  0.322389  0.314400  0.308178  0.295191   \n",
       "\n",
       "   cSM_180s  cSM_240s  cSM_300s  cSM_360s  cSM_480s  cSM_600s  cSM_720s  \\\n",
       "0  0.331738  0.331224  0.330717  0.330219  0.329245  0.328301  0.327383   \n",
       "1  0.333117  0.333045  0.332973  0.332901  0.332758  0.332616  0.332475   \n",
       "2  0.332605  0.332365  0.332127  0.331891  0.331423  0.330961  0.330506   \n",
       "3  0.316285  0.312280  0.308774  0.305711  0.300441  0.296122  0.292450   \n",
       "4  0.286609  0.280245  0.275239  0.271128  0.264667  0.259731  0.255802   \n",
       "\n",
       "   cSM_840s  cSM_960s  cSM_1080s  cSM_1200s  cSM_1500s  cSM_1800s  cSM_2400s  \\\n",
       "0  0.326491  0.325624   0.324782   0.323963   0.322011   0.320173   0.316805   \n",
       "1  0.332334  0.332193   0.332053   0.331914   0.331569   0.331227   0.330552   \n",
       "2  0.330056  0.329612   0.329173   0.328740   0.327680   0.326652   0.324688   \n",
       "3  0.289299  0.286503   0.284045   0.281825   0.277100   0.273229   0.267176   \n",
       "4  0.252532  0.249784   0.247392   0.245327   0.241058   0.237746   0.232842   \n",
       "\n",
       "   cSM_3000s  cSM_3600s  cSM_4500s  cSM_5400s  cSM_6300s  cSM_7200s  \\\n",
       "0   0.313784   0.311040   0.307362   0.304090   0.301175   0.298505   \n",
       "1   0.329891   0.329242   0.328292   0.327366   0.326465   0.325588   \n",
       "2   0.322837   0.321075   0.318594   0.316298   0.314168   0.312169   \n",
       "3   0.262549   0.258777   0.254275   0.250687   0.247689   0.245179   \n",
       "4   0.229331   0.226625   0.223549   0.221242   0.219386   0.217909   \n",
       "\n",
       "   cSM_9000s  cSM_10800s  cSM_14400s  cR_0s   cR_0.1s     cR_1s    cR_20s  \\\n",
       "0   0.293870    0.289895    0.283418    0.5  0.499999  0.499991  0.499819   \n",
       "1   0.323900    0.322285    0.319298    0.5  0.500000  0.499999  0.499976   \n",
       "2   0.308500    0.305247    0.299642    0.5  0.500000  0.499996  0.499917   \n",
       "3   0.241075    0.237877    0.233093    0.5  0.499987  0.499874  0.497568   \n",
       "4   0.215574    0.213859    0.211448    0.5  0.499935  0.499353  0.488959   \n",
       "\n",
       "     cR_40s    cR_60s   cR_120s   cR_180s   cR_240s   cR_300s   cR_360s  \\\n",
       "0  0.499639  0.499459  0.498927  0.498404  0.497887  0.497378  0.496877   \n",
       "1  0.499952  0.499928  0.499855  0.499781  0.499707  0.499632  0.499556   \n",
       "2  0.499826  0.499728  0.499403  0.499051  0.498696  0.498337  0.497980   \n",
       "3  0.495303  0.493191  0.487592  0.482843  0.478725  0.475064  0.471807   \n",
       "4  0.480514  0.473451  0.456658  0.443903  0.433784  0.425542  0.418673   \n",
       "\n",
       "    cR_480s   cR_600s   cR_720s   cR_840s   cR_960s  cR_1080s  cR_1200s  \\\n",
       "0  0.495894  0.494934  0.493990  0.493066  0.492160  0.491270  0.490397   \n",
       "1  0.499402  0.499242  0.499074  0.498900  0.498722  0.498540  0.498354   \n",
       "2  0.497278  0.496586  0.495901  0.495225  0.494558  0.493899  0.493248   \n",
       "3  0.466040  0.461111  0.456740  0.452827  0.449230  0.445941  0.442878   \n",
       "4  0.407761  0.399388  0.392717  0.387176  0.382523  0.378481  0.374997   \n",
       "\n",
       "   cR_1500s  cR_1800s  cR_2400s  cR_3000s  cR_3600s  cR_4500s  cR_5400s  \\\n",
       "0  0.488275  0.486205  0.482223  0.478432  0.474801  0.469672  0.464911   \n",
       "1  0.497878  0.497388  0.496388  0.495405  0.494429  0.492991  0.491599   \n",
       "2  0.491657  0.490113  0.487164  0.484385  0.481740  0.478014  0.474567   \n",
       "3  0.436045  0.430131  0.420319  0.412421  0.405823  0.397754  0.391236   \n",
       "4  0.367811  0.362254  0.354053  0.348201  0.343704  0.338604  0.334787   \n",
       "\n",
       "   cR_6300s  cR_7200s  cR_9000s  cR_10800s  cR_14400s  cB_0s   cB_0.1s  \\\n",
       "0  0.460496  0.456415  0.449129   0.442812   0.432417    1.0  1.000000   \n",
       "1  0.490243  0.488922  0.486377   0.483945   0.479447    1.0  1.000000   \n",
       "2  0.471369  0.468368  0.462860   0.457976   0.449561    1.0  1.000000   \n",
       "3  0.385763  0.381158  0.373627   0.367760   0.359012    1.0  0.999994   \n",
       "4  0.331723  0.329288  0.325444   0.322626   0.318671    1.0  0.999967   \n",
       "\n",
       "      cB_1s    cB_20s    cB_40s    cB_60s   cB_120s   cB_180s   cB_240s  \\\n",
       "0  0.999995  0.999909  0.999819  0.999730  0.999464  0.999202  0.998944   \n",
       "1  0.999999  0.999988  0.999976  0.999964  0.999927  0.999891  0.999854   \n",
       "2  0.999998  0.999958  0.999913  0.999864  0.999702  0.999525  0.999348   \n",
       "3  0.999937  0.998784  0.997652  0.996595  0.993796  0.991422  0.989363   \n",
       "4  0.999676  0.994480  0.990257  0.986725  0.978329  0.971952  0.966892   \n",
       "\n",
       "    cB_300s   cB_360s   cB_480s   cB_600s   cB_720s   cB_840s   cB_960s  \\\n",
       "0  0.998689  0.998438  0.997947  0.997467  0.996995  0.996533  0.996080   \n",
       "1  0.999816  0.999778  0.999701  0.999621  0.999537  0.999450  0.999361   \n",
       "2  0.999169  0.998990  0.998639  0.998293  0.997951  0.997612  0.997279   \n",
       "3  0.987532  0.985904  0.983020  0.980555  0.978370  0.976414  0.974615   \n",
       "4  0.962771  0.959336  0.953881  0.949694  0.946359  0.943588  0.941262   \n",
       "\n",
       "   cB_1080s  cB_1200s  cB_1500s  cB_1800s  cB_2400s  cB_3000s  cB_3600s  \\\n",
       "0  0.995635  0.995198  0.994137  0.993103  0.991112  0.989216  0.987401   \n",
       "1  0.999270  0.999177  0.998939  0.998694  0.998194  0.997703  0.997215   \n",
       "2  0.996949  0.996624  0.995828  0.995057  0.993582  0.992192  0.990870   \n",
       "3  0.972970  0.971439  0.968022  0.965066  0.960159  0.956211  0.952912   \n",
       "4  0.939240  0.937499  0.933905  0.931127  0.927026  0.924100  0.921852   \n",
       "\n",
       "   cB_4500s  cB_5400s  cB_6300s  cB_7200s  cB_9000s  cB_10800s  cB_14400s  \\\n",
       "0  0.984836  0.982456  0.980248  0.978207  0.974564   0.971406   0.966208   \n",
       "1  0.996495  0.995799  0.995121  0.994461  0.993189   0.991972   0.989723   \n",
       "2  0.989007  0.987284  0.985684  0.984184  0.981430   0.978988   0.974781   \n",
       "3  0.948877  0.945618  0.942882  0.940579  0.936814   0.933880   0.929506   \n",
       "4  0.919302  0.917394  0.915861  0.914644  0.912722   0.911313   0.909335   \n",
       "\n",
       "   cC_0s   cC_0.1s     cC_1s    cC_20s    cC_40s    cC_60s   cC_120s  \\\n",
       "0    0.1  0.099999  0.099991  0.099819  0.099639  0.099459  0.098928   \n",
       "1    0.1  0.100000  0.099999  0.099976  0.099952  0.099928  0.099856   \n",
       "2    0.1  0.100000  0.099996  0.099919  0.099842  0.099769  0.099567   \n",
       "3    0.1  0.099987  0.099874  0.097568  0.095306  0.093200  0.087650   \n",
       "4    0.1  0.099935  0.099353  0.089104  0.081342  0.075542  0.064458   \n",
       "\n",
       "    cC_180s   cC_240s   cC_300s   cC_360s   cC_480s   cC_600s   cC_720s  \\\n",
       "0  0.098405  0.097892  0.097386  0.096890  0.095921  0.094985  0.094079   \n",
       "1  0.099784  0.099713  0.099643  0.099574  0.099437  0.099303  0.099175   \n",
       "2  0.099381  0.099199  0.099022  0.098846  0.098495  0.098149  0.097808   \n",
       "3  0.083007  0.079057  0.075629  0.072664  0.067641  0.063628  0.060306   \n",
       "4  0.057962  0.053476  0.050087  0.047356  0.043119  0.039903  0.037344   \n",
       "\n",
       "    cC_840s   cC_960s  cC_1080s  cC_1200s  cC_1500s  cC_1800s  cC_2400s  \\\n",
       "0  0.093203  0.092356  0.091538  0.090746  0.088880  0.087157  0.084096   \n",
       "1  0.099050  0.098929  0.098810  0.098694  0.098414  0.098146  0.097635   \n",
       "2  0.097472  0.097139  0.096810  0.096486  0.095691  0.094921  0.093449   \n",
       "3  0.057535  0.055139  0.053097  0.051298  0.047628  0.044778  0.040604   \n",
       "4  0.035211  0.033414  0.031847  0.030492  0.027681  0.025493  0.022237   \n",
       "\n",
       "   cC_3000s  cC_3600s  cC_4500s  cC_5400s  cC_6300s  cC_7200s  cC_9000s  \\\n",
       "0  0.081459  0.079159  0.076207  0.073680  0.071514  0.069551  0.066241   \n",
       "1  0.097134  0.096649  0.095942  0.095250  0.094577  0.093922  0.092662   \n",
       "2  0.092063  0.090743  0.088883  0.087163  0.085567  0.084069  0.081320   \n",
       "3  0.037613  0.035254  0.032535  0.030413  0.028652  0.027189  0.024798   \n",
       "4  0.019895  0.018086  0.016022  0.014469  0.013218  0.012220  0.010640   \n",
       "\n",
       "   cC_10800s  cC_14400s  cP_0s       cP_0.1s         cP_1s    cP_20s  \\\n",
       "0   0.063437   0.058919    0.0  4.542286e-07  4.541710e-06  0.000091   \n",
       "1   0.091455   0.089223    0.0  6.043392e-08  6.043290e-07  0.000012   \n",
       "2   0.078883   0.074683    0.0  2.046509e-07  2.046392e-06  0.000041   \n",
       "3   0.022935   0.020134    0.0  6.313183e-06  6.302088e-05  0.001216   \n",
       "4   0.009476   0.007837    0.0  3.266661e-05  3.237267e-04  0.005472   \n",
       "\n",
       "     cP_40s    cP_60s   cP_120s   cP_180s   cP_240s   cP_300s   cP_360s  \\\n",
       "0  0.000181  0.000270  0.000536  0.000798  0.001055  0.001308  0.001557   \n",
       "1  0.000024  0.000036  0.000072  0.000108  0.000144  0.000180  0.000216   \n",
       "2  0.000082  0.000122  0.000244  0.000364  0.000484  0.000603  0.000721   \n",
       "3  0.002347  0.003402  0.006185  0.008524  0.010527  0.012280  0.013811   \n",
       "4  0.009467  0.012578  0.019071  0.023362  0.026544  0.029047  0.031102   \n",
       "\n",
       "    cP_480s   cP_600s   cP_720s   cP_840s   cP_960s  cP_1080s  cP_1200s  \\\n",
       "0  0.002044  0.002516  0.002975  0.003421  0.003855  0.004276  0.004685   \n",
       "1  0.000288  0.000359  0.000429  0.000500  0.000570  0.000640  0.000710   \n",
       "2  0.000955  0.001186  0.001414  0.001639  0.001861  0.002080  0.002297   \n",
       "3  0.016446  0.018605  0.020441  0.022017  0.023415  0.024644  0.025754   \n",
       "4  0.034333  0.036801  0.038766  0.040400  0.041775  0.042971  0.044003   \n",
       "\n",
       "   cP_1500s  cP_1800s  cP_2400s  cP_3000s  cP_3600s  cP_4500s  cP_5400s  \\\n",
       "0  0.005661  0.006580  0.008264  0.009775  0.011147  0.012986  0.014621   \n",
       "1  0.000882  0.001053  0.001390  0.001721  0.002046  0.002521  0.002984   \n",
       "2  0.002827  0.003341  0.004323  0.005248  0.006129  0.007370  0.008518   \n",
       "3  0.028117  0.030052  0.033079  0.035392  0.037278  0.039529  0.041323   \n",
       "4  0.046138  0.047793  0.050245  0.052001  0.053354  0.054892  0.056046   \n",
       "\n",
       "   cP_6300s  cP_7200s  cP_9000s  cP_10800s  cP_14400s  cIMP1_0s  cIMP1_0.1s  \\\n",
       "0  0.016079  0.017414  0.019731   0.021719   0.024957       0.0         0.0   \n",
       "1  0.003434  0.003872  0.004717   0.005524   0.007018       0.0         0.0   \n",
       "2  0.009583  0.010582  0.012417   0.014043   0.016845       0.0         0.0   \n",
       "3  0.042822  0.044077  0.046129   0.047728   0.050120       0.0         0.0   \n",
       "4  0.056974  0.057712  0.058880   0.059737   0.060942       0.0         0.0   \n",
       "\n",
       "   cIMP1_1s  cIMP1_20s  cIMP1_40s  cIMP1_60s  cIMP1_120s  cIMP1_180s  \\\n",
       "0       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "1       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "2       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "3       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "4       0.0        0.0        0.0        0.0         0.0         0.0   \n",
       "\n",
       "   cIMP1_240s  cIMP1_300s  cIMP1_360s  cIMP1_480s  cIMP1_600s  cIMP1_720s  \\\n",
       "0         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "   cIMP1_840s  cIMP1_960s  cIMP1_1080s  cIMP1_1200s  cIMP1_1500s  cIMP1_1800s  \\\n",
       "0         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "1         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "2         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "3         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "4         0.0         0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   cIMP1_2400s  cIMP1_3000s  cIMP1_3600s  cIMP1_4500s  cIMP1_5400s  \\\n",
       "0          0.0          0.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          0.0          0.0          0.0   \n",
       "2          0.0          0.0          0.0          0.0          0.0   \n",
       "3          0.0          0.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   cIMP1_6300s  cIMP1_7200s  cIMP1_9000s  cIMP1_10800s  cIMP1_14400s  \\\n",
       "0          0.0          0.0          0.0           0.0           0.0   \n",
       "1          0.0          0.0          0.0           0.0           0.0   \n",
       "2          0.0          0.0          0.0           0.0           0.0   \n",
       "3          0.0          0.0          0.0           0.0           0.0   \n",
       "4          0.0          0.0          0.0           0.0           0.0   \n",
       "\n",
       "   cIMP2_0s    cIMP2_0.1s      cIMP2_1s     cIMP2_20s     cIMP2_40s  \\\n",
       "0       0.0  8.967202e-17  8.989862e-14  7.744688e-10  6.273261e-09   \n",
       "1       0.0  1.087332e-16  1.087824e-13  9.560027e-10  7.810140e-09   \n",
       "2       0.0  6.766025e-14  6.912635e-11  7.050067e-07  5.285923e-06   \n",
       "3       0.0  1.572048e-14  1.623255e-11  1.396851e-07  1.072748e-06   \n",
       "4       0.0  8.461659e-12  9.576828e-09  4.840517e-05  2.760846e-04   \n",
       "\n",
       "      cIMP2_60s    cIMP2_120s    cIMP2_180s  cIMP2_240s  cIMP2_300s  \\\n",
       "0  2.012814e-08  1.808198e-07  6.460319e-07    0.000001    0.000003   \n",
       "1  2.497200e-08  2.488103e-07  9.247170e-07    0.000002    0.000004   \n",
       "2  1.346008e-05  5.464402e-05  1.101606e-04    0.000168    0.000228   \n",
       "3  3.086097e-06  1.917127e-05  5.449461e-05    0.000111    0.000188   \n",
       "4  6.969441e-04  2.600025e-03  4.686422e-03    0.006564    0.008181   \n",
       "\n",
       "   cIMP2_360s  cIMP2_480s  cIMP2_600s  cIMP2_720s  cIMP2_840s  cIMP2_960s  \\\n",
       "0    0.000004    0.000009    0.000017    0.000029    0.000046    0.000066   \n",
       "1    0.000006    0.000012    0.000020    0.000034    0.000050    0.000069   \n",
       "2    0.000289    0.000406    0.000521    0.000636    0.000749    0.000860   \n",
       "3    0.000285    0.000533    0.000839    0.001188    0.001569    0.001970   \n",
       "4    0.009561    0.011786    0.013505    0.014876    0.016011    0.016964   \n",
       "\n",
       "   cIMP2_1080s  cIMP2_1200s  cIMP2_1500s  cIMP2_1800s  cIMP2_2400s  \\\n",
       "0     0.000089     0.000116     0.000202     0.000317     0.000624   \n",
       "1     0.000090     0.000113     0.000179     0.000252     0.000416   \n",
       "2     0.000970     0.001079     0.001345     0.001603     0.002095   \n",
       "3     0.002385     0.002807     0.003861     0.004882     0.006762   \n",
       "4     0.017789     0.018498     0.019957     0.021079     0.022728   \n",
       "\n",
       "   cIMP2_3000s  cIMP2_3600s  cIMP2_4500s  cIMP2_5400s  cIMP2_6300s  \\\n",
       "0     0.001009     0.001452     0.002178     0.002923     0.003673   \n",
       "1     0.000576     0.000740     0.000984     0.001217     0.001445   \n",
       "2     0.002559     0.003001     0.003623     0.004199     0.004733   \n",
       "3     0.008397     0.009810     0.011594     0.013059     0.014296   \n",
       "4     0.023898     0.024794     0.025806     0.026561     0.027165   \n",
       "\n",
       "   cIMP2_7200s  cIMP2_9000s  cIMP2_10800s  cIMP2_14400s  cINT1_0s  \\\n",
       "0     0.004379     0.005704      0.006875      0.008834       0.0   \n",
       "1     0.001667     0.002095      0.002503      0.003259       0.0   \n",
       "2     0.005234     0.006153      0.006969      0.008374       0.0   \n",
       "3     0.015344     0.017057      0.018392      0.020374       0.0   \n",
       "4     0.027644     0.028399      0.028950      0.029722       0.0   \n",
       "\n",
       "     cINT1_0.1s      cINT1_1s  cINT1_20s  cINT1_40s  cINT1_60s  cINT1_120s  \\\n",
       "0  4.542286e-07  4.541710e-06   0.000091   0.000181   0.000270    0.000536   \n",
       "1  6.043392e-08  6.043288e-07   0.000012   0.000024   0.000036    0.000072   \n",
       "2  2.046508e-07  2.046254e-06   0.000039   0.000071   0.000095    0.000134   \n",
       "3  6.313183e-06  6.302085e-05   0.001216   0.002345   0.003395    0.006146   \n",
       "4  3.266660e-05  3.237075e-04   0.005375   0.008915   0.011184    0.013871   \n",
       "\n",
       "   cINT1_180s  cINT1_240s  cINT1_300s  cINT1_360s  cINT1_480s  cINT1_600s  \\\n",
       "0    0.000796    0.001052    0.001303    0.001549    0.002026    0.002483   \n",
       "1    0.000107    0.000140    0.000173    0.000205    0.000264    0.000318   \n",
       "2    0.000144    0.000149    0.000146    0.000143    0.000144    0.000143   \n",
       "3    0.008415    0.010305    0.011903    0.013240    0.015379    0.016927   \n",
       "4    0.013989    0.013416    0.012685    0.011980    0.010762    0.009791   \n",
       "\n",
       "   cINT1_720s  cINT1_840s  cINT1_960s  cINT1_1080s  cINT1_1200s  cINT1_1500s  \\\n",
       "0    0.002916    0.003330    0.003723     0.004097     0.004452     0.005258   \n",
       "1    0.000362    0.000400    0.000433     0.000460     0.000483     0.000525   \n",
       "2    0.000142    0.000140    0.000140     0.000139     0.000139     0.000137   \n",
       "3    0.018065    0.018878    0.019476     0.019873     0.020141     0.020394   \n",
       "4    0.009015    0.008378    0.007848     0.007393     0.007007     0.006224   \n",
       "\n",
       "   cINT1_1800s  cINT1_2400s  cINT1_3000s  cINT1_3600s  cINT1_4500s  \\\n",
       "0     0.005946     0.007016     0.007757     0.008242     0.008630   \n",
       "1     0.000548     0.000559     0.000569     0.000566     0.000553   \n",
       "2     0.000136     0.000133     0.000130     0.000127     0.000124   \n",
       "3     0.020287     0.019555     0.018597     0.017658     0.016342   \n",
       "4     0.005635     0.004789     0.004205     0.003766     0.003280   \n",
       "\n",
       "   cINT1_5400s  cINT1_6300s  cINT1_7200s  cINT1_9000s  cINT1_10800s  \\\n",
       "0     0.008775     0.008734     0.008657     0.008323      0.007969   \n",
       "1     0.000549     0.000545     0.000539     0.000527      0.000517   \n",
       "2     0.000120     0.000117     0.000115     0.000110      0.000105   \n",
       "3     0.015205     0.014230     0.013390     0.012016      0.010945   \n",
       "4     0.002925     0.002643     0.002424     0.002082      0.001837   \n",
       "\n",
       "   cINT1_14400s  \n",
       "0      0.007289  \n",
       "1      0.000500  \n",
       "2      0.000098  \n",
       "3      0.009372  \n",
       "4      0.001498  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>(93, 254)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism\n",
       "1933581  (93, 254)\n",
       "1933582  (93, 254)\n",
       "1933583  (93, 254)\n",
       "1933584  (93, 254)\n",
       "1933585  (93, 254)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df = df.iloc[:, -1:]\n",
    "y_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "unique_values_y_df = y_df['Mechanism'].unique()\n",
    "print(len(unique_values_y_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mechanism\n",
      "0                1\n",
      "1                1\n",
      "2                1\n",
      "3                1\n",
      "4                1\n",
      "...            ...\n",
      "1933581         62\n",
      "1933582         62\n",
      "1933583         62\n",
      "1933584         62\n",
      "1933585         62\n",
      "\n",
      "[1933586 rows x 1 columns]\n",
      "{'(105, 197)': 1, '(105, 228)': 2, '(105, 229)': 3, '(108, 138)': 4, '(108, 170)': 5, '(108, 192)': 6, '(108, 224)': 7, '(108, 246)': 8, '(108, 278)': 9, '(108, 300)': 10, '(108, 332)': 11, '(109, 138)': 12, '(109, 139)': 13, '(109, 170)': 14, '(109, 171)': 15, '(109, 192)': 16, '(109, 193)': 17, '(109, 224)': 18, '(109, 225)': 19, '(93, 255)': 20, '(93, 262)': 21, '(93, 263)': 22, '(93, 308)': 23, '(93, 309)': 24, '(93, 316)': 25, '(93, 317)': 26, '(97, 128)': 27, '(97, 166)': 28, '(97, 167)': 29, '(97, 181)': 30, '(97, 220)': 31, '(97, 221)': 32, '(72, 204)': 33, '(72, 205)': 34, '(72, 212)': 35, '(72, 213)': 36, '(73, 150)': 37, '(73, 151)': 38, '(73, 158)': 39, '(73, 159)': 40, '(73, 204)': 41, '(73, 205)': 42, '(73, 212)': 43, '(73, 213)': 44, '(76, 146)': 45, '(76, 147)': 46, '(76, 154)': 47, '(76, 155)': 48, '(76, 200)': 49, '(76, 201)': 50, '(76, 208)': 51, '(76, 209)': 52, '(76, 254)': 53, '(76, 255)': 54, '(76, 262)': 55, '(76, 263)': 56, '(76, 308)': 57, '(76, 309)': 58, '(76, 316)': 59, '(76, 317)': 60, '(93, 209)': 61, '(93, 254)': 62, '(109, 332)': 63, '(109, 333)': 64, '(113, 128)': 65, '(113, 166)': 66, '(113, 167)': 67, '(113, 181)': 68, '(113, 220)': 69, '(113, 221)': 70, '(113, 338)': 71, '(113, 386)': 72, '(113, 404)': 73, '(113, 452)': 74, '(117, 126)': 75, '(117, 162)': 76, '(117, 163)': 77, '(125, 225)': 78, '(125, 246)': 79, '(125, 247)': 80, '(125, 278)': 81, '(125, 279)': 82, '(125, 300)': 83, '(125, 301)': 84, '(125, 332)': 85, '(92, 201)': 86, '(92, 208)': 87, '(92, 209)': 88, '(92, 254)': 89, '(92, 255)': 90, '(92, 262)': 91, '(92, 263)': 92, '(92, 308)': 93, '(92, 309)': 94, '(92, 316)': 95, '(92, 317)': 96, '(93, 146)': 97, '(93, 147)': 98, '(93, 154)': 99, '(93, 155)': 100, '(93, 200)': 101, '(93, 201)': 102, '(93, 208)': 103, '(45, 147)': 104, '(45, 154)': 105, '(45, 155)': 106, '(45, 254)': 107, '(45, 255)': 108, '(45, 262)': 109, '(45, 263)': 110, '(50, 150)': 111, '(50, 151)': 112, '(50, 158)': 113, '(50, 159)': 114, '(51, 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170, '(88, 204)': 171, '(88, 205)': 172, '(88, 212)': 173, '(88, 213)': 174, '(89, 150)': 175, '(89, 151)': 176, '(89, 158)': 177, '(89, 159)': 178, '(89, 204)': 179, '(89, 205)': 180, '(89, 212)': 181, '(89, 213)': 182, '(92, 146)': 183, '(92, 147)': 184, '(92, 154)': 185, '(92, 155)': 186, '(92, 200)': 187, '(72, 159)': 188, '(33, 139)': 189, '(33, 170)': 190, '(33, 171)': 191, '(33, 192)': 192, '(33, 193)': 193, '(33, 224)': 194, '(33, 225)': 195, '(37, 126)': 196, '(37, 162)': 197, '(37, 163)': 198, '(37, 179)': 199, '(37, 216)': 200, '(37, 217)': 201, '(37, 334)': 202, '(37, 382)': 203, '(37, 400)': 204, '(37, 448)': 205, '(40, 138)': 206, '(40, 170)': 207, '(40, 192)': 208, '(40, 224)': 209, '(41, 138)': 210, '(41, 139)': 211, '(41, 170)': 212, '(41, 171)': 213, '(41, 192)': 214, '(41, 193)': 215, '(41, 224)': 216, '(41, 225)': 217, '(42, 150)': 218, '(42, 151)': 219, '(42, 158)': 220, '(42, 159)': 221, '(43, 150)': 222, '(43, 151)': 223, '(43, 158)': 224, '(43, 159)': 225, '(44, 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146)': 283, '(17, 147)': 284, '(17, 154)': 285, '(17, 155)': 286, '(17, 200)': 287, '(17, 201)': 288, '(17, 208)': 289, '(17, 209)': 290, '(24, 146)': 291, '(24, 147)': 292, '(24, 154)': 293, '(24, 155)': 294, '(24, 200)': 295, '(24, 201)': 296, '(24, 208)': 297, '(24, 209)': 298, '(25, 146)': 299, '(25, 147)': 300, '(25, 154)': 301, '(25, 155)': 302, '(25, 200)': 303, '(25, 201)': 304, '(25, 208)': 305, '(25, 209)': 306, '(29, 126)': 307, '(29, 162)': 308, '(29, 163)': 309, '(29, 179)': 310, '(29, 216)': 311, '(29, 217)': 312, '(29, 334)': 313, '(29, 382)': 314, '(29, 400)': 315, '(29, 448)': 316, '(32, 138)': 317, '(32, 170)': 318, '(32, 192)': 319, '(32, 224)': 320, '(33, 138)': 321, '(109, 246)': 322, '(109, 247)': 323, '(109, 278)': 324, '(109, 279)': 325, '(109, 300)': 326, '(109, 301)': 327, '(117, 514)': 328, '(117, 532)': 329, '(117, 580)': 330, '(120, 142)': 331, '(120, 174)': 332, '(120, 196)': 333, '(120, 228)': 334, '(121, 142)': 335, '(121, 143)': 336, '(121, 174)': 337, '(121, 175)': 338, '(121, 196)': 339, '(121, 197)': 340, '(121, 228)': 341, '(121, 229)': 342, '(77, 146)': 343, '(77, 147)': 344, '(77, 154)': 345, '(77, 155)': 346, '(77, 200)': 347, '(77, 201)': 348, '(77, 208)': 349, '(77, 209)': 350, '(77, 254)': 351, '(77, 255)': 352, '(77, 262)': 353, '(77, 263)': 354, '(77, 308)': 355, '(77, 309)': 356, '(125, 333)': 357, '(117, 217)': 358, '(117, 232)': 359, '(117, 270)': 360, '(117, 271)': 361, '(117, 285)': 362, '(117, 324)': 363, '(117, 325)': 364, '(117, 334)': 365, '(117, 382)': 366, '(117, 400)': 367, '(117, 448)': 368, '(117, 466)': 369, '(97, 338)': 370, '(97, 386)': 371, '(97, 404)': 372, '(97, 452)': 373, '(101, 126)': 374, '(101, 162)': 375, '(101, 163)': 376, '(101, 179)': 377, '(101, 216)': 378, '(101, 217)': 379, '(101, 232)': 380, '(101, 270)': 381, '(101, 271)': 382, '(101, 285)': 383, '(101, 324)': 384, '(101, 325)': 385, '(101, 334)': 386, '(101, 382)': 387, '(101, 400)': 388, '(101, 448)': 389, '(101, 466)': 390, '(101, 514)': 391, '(101, 532)': 392, '(101, 580)': 393, '(104, 142)': 394, '(104, 174)': 395, '(104, 196)': 396, '(104, 228)': 397, '(105, 142)': 398, '(105, 143)': 399, '(105, 174)': 400, '(105, 175)': 401, '(105, 196)': 402, '(124, 138)': 403, '(124, 170)': 404, '(124, 192)': 405, '(124, 224)': 406, '(124, 246)': 407, '(124, 278)': 408, '(124, 300)': 409, '(124, 332)': 410, '(125, 138)': 411, '(125, 139)': 412, '(125, 170)': 413, '(125, 171)': 414, '(125, 192)': 415, '(125, 193)': 416, '(125, 224)': 417, '(61, 247)': 418, '(61, 278)': 419, '(61, 279)': 420, '(63, 128)': 421, '(63, 166)': 422, '(63, 167)': 423, '(63, 338)': 424, '(63, 386)': 425, '(65, 126)': 426, '(65, 162)': 427, '(65, 163)': 428, '(65, 232)': 429, '(65, 270)': 430, '(65, 271)': 431, '(65, 334)': 432, '(65, 382)': 433, '(65, 466)': 434, '(65, 514)': 435, '(66, 142)': 436, '(66, 174)': 437, '(67, 142)': 438, '(67, 143)': 439, '(67, 174)': 440, '(67, 175)': 441, '(68, 138)': 442, '(68, 170)': 443, '(68, 246)': 444, '(68, 278)': 445, '(69, 138)': 446, '(69, 139)': 447, '(69, 170)': 448, '(69, 171)': 449, '(69, 246)': 450, '(69, 247)': 451, '(69, 278)': 452, '(69, 279)': 453, '(72, 150)': 454, '(72, 151)': 455, '(72, 158)': 456}\n"
     ]
    }
   ],
   "source": [
    "# Create a copy of the original DataFrame\n",
    "y_df_factorized = y_df.copy()\n",
    "\n",
    "# Create a dictionary to store the mapping of original labels to sequential labels\n",
    "mechanism_dic = {}\n",
    "\n",
    "# Use factorize to get sequential labels\n",
    "y_df_factorized['Mechanism'], unique_labels = pd.factorize(y_df_factorized['Mechanism'])\n",
    "\n",
    "# Increment by 1 to match your requirement\n",
    "y_df_factorized['Mechanism'] += 1\n",
    "\n",
    "# Store the mapping in the dictionary\n",
    "mechanism_dic = dict(zip(unique_labels, range(1, len(unique_labels) + 1)))\n",
    "\n",
    "# Display the DataFrame with sequential labels\n",
    "print(y_df_factorized)\n",
    "\n",
    "# Display the dictionary\n",
    "print(mechanism_dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456\n"
     ]
    }
   ],
   "source": [
    "print(len(mechanism_dic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 2. ML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.1 Split Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>cSM_360s</th>\n",
       "      <th>cSM_480s</th>\n",
       "      <th>cSM_600s</th>\n",
       "      <th>cSM_720s</th>\n",
       "      <th>cSM_840s</th>\n",
       "      <th>cSM_960s</th>\n",
       "      <th>cSM_1080s</th>\n",
       "      <th>cSM_1200s</th>\n",
       "      <th>cSM_1500s</th>\n",
       "      <th>cSM_1800s</th>\n",
       "      <th>cSM_2400s</th>\n",
       "      <th>cSM_3000s</th>\n",
       "      <th>cSM_3600s</th>\n",
       "      <th>cSM_4500s</th>\n",
       "      <th>cSM_5400s</th>\n",
       "      <th>cSM_6300s</th>\n",
       "      <th>cSM_7200s</th>\n",
       "      <th>cSM_9000s</th>\n",
       "      <th>cSM_10800s</th>\n",
       "      <th>cSM_14400s</th>\n",
       "      <th>cR_0s</th>\n",
       "      <th>cR_0.1s</th>\n",
       "      <th>cR_1s</th>\n",
       "      <th>cR_20s</th>\n",
       "      <th>cR_40s</th>\n",
       "      <th>cR_60s</th>\n",
       "      <th>cR_120s</th>\n",
       "      <th>cR_180s</th>\n",
       "      <th>cR_240s</th>\n",
       "      <th>cR_300s</th>\n",
       "      <th>cR_360s</th>\n",
       "      <th>cR_480s</th>\n",
       "      <th>cR_600s</th>\n",
       "      <th>cR_720s</th>\n",
       "      <th>cR_840s</th>\n",
       "      <th>cR_960s</th>\n",
       "      <th>cR_1080s</th>\n",
       "      <th>cR_1200s</th>\n",
       "      <th>cR_1500s</th>\n",
       "      <th>cR_1800s</th>\n",
       "      <th>cR_2400s</th>\n",
       "      <th>cR_3000s</th>\n",
       "      <th>cR_3600s</th>\n",
       "      <th>cR_4500s</th>\n",
       "      <th>cR_5400s</th>\n",
       "      <th>cR_6300s</th>\n",
       "      <th>cR_7200s</th>\n",
       "      <th>cR_9000s</th>\n",
       "      <th>cR_10800s</th>\n",
       "      <th>cR_14400s</th>\n",
       "      <th>cB_0s</th>\n",
       "      <th>cB_0.1s</th>\n",
       "      <th>cB_1s</th>\n",
       "      <th>cB_20s</th>\n",
       "      <th>cB_40s</th>\n",
       "      <th>cB_60s</th>\n",
       "      <th>cB_120s</th>\n",
       "      <th>cB_180s</th>\n",
       "      <th>cB_240s</th>\n",
       "      <th>cB_300s</th>\n",
       "      <th>cB_360s</th>\n",
       "      <th>cB_480s</th>\n",
       "      <th>cB_600s</th>\n",
       "      <th>cB_720s</th>\n",
       "      <th>cB_840s</th>\n",
       "      <th>cB_960s</th>\n",
       "      <th>cB_1080s</th>\n",
       "      <th>cB_1200s</th>\n",
       "      <th>cB_1500s</th>\n",
       "      <th>cB_1800s</th>\n",
       "      <th>cB_2400s</th>\n",
       "      <th>cB_3000s</th>\n",
       "      <th>cB_3600s</th>\n",
       "      <th>cB_4500s</th>\n",
       "      <th>cB_5400s</th>\n",
       "      <th>cB_6300s</th>\n",
       "      <th>cB_7200s</th>\n",
       "      <th>cB_9000s</th>\n",
       "      <th>cB_10800s</th>\n",
       "      <th>cB_14400s</th>\n",
       "      <th>cC_0s</th>\n",
       "      <th>cC_0.1s</th>\n",
       "      <th>cC_1s</th>\n",
       "      <th>cC_20s</th>\n",
       "      <th>cC_40s</th>\n",
       "      <th>cC_60s</th>\n",
       "      <th>cC_120s</th>\n",
       "      <th>cC_180s</th>\n",
       "      <th>cC_240s</th>\n",
       "      <th>cC_300s</th>\n",
       "      <th>cC_360s</th>\n",
       "      <th>cC_480s</th>\n",
       "      <th>cC_600s</th>\n",
       "      <th>cC_720s</th>\n",
       "      <th>cC_840s</th>\n",
       "      <th>cC_960s</th>\n",
       "      <th>cC_1080s</th>\n",
       "      <th>cC_1200s</th>\n",
       "      <th>cC_1500s</th>\n",
       "      <th>cC_1800s</th>\n",
       "      <th>cC_2400s</th>\n",
       "      <th>cC_3000s</th>\n",
       "      <th>cC_3600s</th>\n",
       "      <th>cC_4500s</th>\n",
       "      <th>cC_5400s</th>\n",
       "      <th>cC_6300s</th>\n",
       "      <th>cC_7200s</th>\n",
       "      <th>cC_9000s</th>\n",
       "      <th>cC_10800s</th>\n",
       "      <th>cC_14400s</th>\n",
       "      <th>cP_0s</th>\n",
       "      <th>cP_0.1s</th>\n",
       "      <th>cP_1s</th>\n",
       "      <th>cP_20s</th>\n",
       "      <th>cP_40s</th>\n",
       "      <th>cP_60s</th>\n",
       "      <th>cP_120s</th>\n",
       "      <th>cP_180s</th>\n",
       "      <th>cP_240s</th>\n",
       "      <th>cP_300s</th>\n",
       "      <th>cP_360s</th>\n",
       "      <th>cP_480s</th>\n",
       "      <th>cP_600s</th>\n",
       "      <th>cP_720s</th>\n",
       "      <th>cP_840s</th>\n",
       "      <th>cP_960s</th>\n",
       "      <th>cP_1080s</th>\n",
       "      <th>cP_1200s</th>\n",
       "      <th>cP_1500s</th>\n",
       "      <th>cP_1800s</th>\n",
       "      <th>cP_2400s</th>\n",
       "      <th>cP_3000s</th>\n",
       "      <th>cP_3600s</th>\n",
       "      <th>cP_4500s</th>\n",
       "      <th>cP_5400s</th>\n",
       "      <th>cP_6300s</th>\n",
       "      <th>cP_7200s</th>\n",
       "      <th>cP_9000s</th>\n",
       "      <th>cP_10800s</th>\n",
       "      <th>cP_14400s</th>\n",
       "      <th>cIMP1_0s</th>\n",
       "      <th>cIMP1_0.1s</th>\n",
       "      <th>cIMP1_1s</th>\n",
       "      <th>cIMP1_20s</th>\n",
       "      <th>cIMP1_40s</th>\n",
       "      <th>cIMP1_60s</th>\n",
       "      <th>cIMP1_120s</th>\n",
       "      <th>cIMP1_180s</th>\n",
       "      <th>cIMP1_240s</th>\n",
       "      <th>cIMP1_300s</th>\n",
       "      <th>cIMP1_360s</th>\n",
       "      <th>cIMP1_480s</th>\n",
       "      <th>cIMP1_600s</th>\n",
       "      <th>cIMP1_720s</th>\n",
       "      <th>cIMP1_840s</th>\n",
       "      <th>cIMP1_960s</th>\n",
       "      <th>cIMP1_1080s</th>\n",
       "      <th>cIMP1_1200s</th>\n",
       "      <th>cIMP1_1500s</th>\n",
       "      <th>cIMP1_1800s</th>\n",
       "      <th>cIMP1_2400s</th>\n",
       "      <th>cIMP1_3000s</th>\n",
       "      <th>cIMP1_3600s</th>\n",
       "      <th>cIMP1_4500s</th>\n",
       "      <th>cIMP1_5400s</th>\n",
       "      <th>cIMP1_6300s</th>\n",
       "      <th>cIMP1_7200s</th>\n",
       "      <th>cIMP1_9000s</th>\n",
       "      <th>cIMP1_10800s</th>\n",
       "      <th>cIMP1_14400s</th>\n",
       "      <th>cIMP2_0s</th>\n",
       "      <th>cIMP2_0.1s</th>\n",
       "      <th>cIMP2_1s</th>\n",
       "      <th>cIMP2_20s</th>\n",
       "      <th>cIMP2_40s</th>\n",
       "      <th>cIMP2_60s</th>\n",
       "      <th>cIMP2_120s</th>\n",
       "      <th>cIMP2_180s</th>\n",
       "      <th>cIMP2_240s</th>\n",
       "      <th>cIMP2_300s</th>\n",
       "      <th>cIMP2_360s</th>\n",
       "      <th>cIMP2_480s</th>\n",
       "      <th>cIMP2_600s</th>\n",
       "      <th>cIMP2_720s</th>\n",
       "      <th>cIMP2_840s</th>\n",
       "      <th>cIMP2_960s</th>\n",
       "      <th>cIMP2_1080s</th>\n",
       "      <th>cIMP2_1200s</th>\n",
       "      <th>cIMP2_1500s</th>\n",
       "      <th>cIMP2_1800s</th>\n",
       "      <th>cIMP2_2400s</th>\n",
       "      <th>cIMP2_3000s</th>\n",
       "      <th>cIMP2_3600s</th>\n",
       "      <th>cIMP2_4500s</th>\n",
       "      <th>cIMP2_5400s</th>\n",
       "      <th>cIMP2_6300s</th>\n",
       "      <th>cIMP2_7200s</th>\n",
       "      <th>cIMP2_9000s</th>\n",
       "      <th>cIMP2_10800s</th>\n",
       "      <th>cIMP2_14400s</th>\n",
       "      <th>cINT1_0s</th>\n",
       "      <th>cINT1_0.1s</th>\n",
       "      <th>cINT1_1s</th>\n",
       "      <th>cINT1_20s</th>\n",
       "      <th>cINT1_40s</th>\n",
       "      <th>cINT1_60s</th>\n",
       "      <th>cINT1_120s</th>\n",
       "      <th>cINT1_180s</th>\n",
       "      <th>cINT1_240s</th>\n",
       "      <th>cINT1_300s</th>\n",
       "      <th>cINT1_360s</th>\n",
       "      <th>cINT1_480s</th>\n",
       "      <th>cINT1_600s</th>\n",
       "      <th>cINT1_720s</th>\n",
       "      <th>cINT1_840s</th>\n",
       "      <th>cINT1_960s</th>\n",
       "      <th>cINT1_1080s</th>\n",
       "      <th>cINT1_1200s</th>\n",
       "      <th>cINT1_1500s</th>\n",
       "      <th>cINT1_1800s</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.327349</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.202480</td>\n",
       "      <td>0.185327</td>\n",
       "      <td>0.176645</td>\n",
       "      <td>0.164088</td>\n",
       "      <td>0.158029</td>\n",
       "      <td>0.154277</td>\n",
       "      <td>0.151664</td>\n",
       "      <td>0.149713</td>\n",
       "      <td>0.146951</td>\n",
       "      <td>0.145067</td>\n",
       "      <td>0.143684</td>\n",
       "      <td>0.142618</td>\n",
       "      <td>0.141768</td>\n",
       "      <td>0.141068</td>\n",
       "      <td>0.140488</td>\n",
       "      <td>0.139372</td>\n",
       "      <td>0.138569</td>\n",
       "      <td>0.137491</td>\n",
       "      <td>0.136792</td>\n",
       "      <td>0.136298</td>\n",
       "      <td>0.135781</td>\n",
       "      <td>0.135420</td>\n",
       "      <td>0.135155</td>\n",
       "      <td>0.134949</td>\n",
       "      <td>0.134653</td>\n",
       "      <td>0.134450</td>\n",
       "      <td>0.134189</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.494015</td>\n",
       "      <td>0.460257</td>\n",
       "      <td>0.369147</td>\n",
       "      <td>0.351994</td>\n",
       "      <td>0.343312</td>\n",
       "      <td>0.330755</td>\n",
       "      <td>0.324695</td>\n",
       "      <td>0.320944</td>\n",
       "      <td>0.318331</td>\n",
       "      <td>0.316380</td>\n",
       "      <td>0.313618</td>\n",
       "      <td>0.311734</td>\n",
       "      <td>0.310350</td>\n",
       "      <td>0.309285</td>\n",
       "      <td>0.308435</td>\n",
       "      <td>0.307735</td>\n",
       "      <td>0.307155</td>\n",
       "      <td>0.306038</td>\n",
       "      <td>0.305235</td>\n",
       "      <td>0.304157</td>\n",
       "      <td>0.303458</td>\n",
       "      <td>0.302965</td>\n",
       "      <td>0.302447</td>\n",
       "      <td>0.302086</td>\n",
       "      <td>0.301821</td>\n",
       "      <td>0.301615</td>\n",
       "      <td>0.301319</td>\n",
       "      <td>0.301117</td>\n",
       "      <td>0.300856</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.997008</td>\n",
       "      <td>0.980128</td>\n",
       "      <td>0.934573</td>\n",
       "      <td>0.925997</td>\n",
       "      <td>0.921656</td>\n",
       "      <td>0.915378</td>\n",
       "      <td>0.912348</td>\n",
       "      <td>0.910472</td>\n",
       "      <td>0.909165</td>\n",
       "      <td>0.908190</td>\n",
       "      <td>0.906809</td>\n",
       "      <td>0.905867</td>\n",
       "      <td>0.905175</td>\n",
       "      <td>0.904642</td>\n",
       "      <td>0.904217</td>\n",
       "      <td>0.903867</td>\n",
       "      <td>0.903577</td>\n",
       "      <td>0.903019</td>\n",
       "      <td>0.902618</td>\n",
       "      <td>0.902079</td>\n",
       "      <td>0.901729</td>\n",
       "      <td>0.901482</td>\n",
       "      <td>0.901224</td>\n",
       "      <td>0.901043</td>\n",
       "      <td>0.900911</td>\n",
       "      <td>0.900808</td>\n",
       "      <td>0.900660</td>\n",
       "      <td>0.900558</td>\n",
       "      <td>0.900428</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.097008</td>\n",
       "      <td>0.080128</td>\n",
       "      <td>0.034573</td>\n",
       "      <td>0.025997</td>\n",
       "      <td>0.021656</td>\n",
       "      <td>0.015378</td>\n",
       "      <td>0.012348</td>\n",
       "      <td>0.010472</td>\n",
       "      <td>0.009165</td>\n",
       "      <td>0.008190</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.005867</td>\n",
       "      <td>0.005175</td>\n",
       "      <td>0.004642</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.003867</td>\n",
       "      <td>0.003577</td>\n",
       "      <td>0.003019</td>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.002079</td>\n",
       "      <td>0.001729</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>0.000808</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.000558</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166703</td>\n",
       "      <td>0.168370</td>\n",
       "      <td>0.169879</td>\n",
       "      <td>0.171165</td>\n",
       "      <td>0.174245</td>\n",
       "      <td>0.176641</td>\n",
       "      <td>0.178627</td>\n",
       "      <td>0.180334</td>\n",
       "      <td>0.181834</td>\n",
       "      <td>0.184390</td>\n",
       "      <td>0.186522</td>\n",
       "      <td>0.188352</td>\n",
       "      <td>0.189955</td>\n",
       "      <td>0.191382</td>\n",
       "      <td>0.192668</td>\n",
       "      <td>0.193835</td>\n",
       "      <td>0.196360</td>\n",
       "      <td>0.198464</td>\n",
       "      <td>0.201830</td>\n",
       "      <td>0.204458</td>\n",
       "      <td>0.206607</td>\n",
       "      <td>0.209219</td>\n",
       "      <td>0.211331</td>\n",
       "      <td>0.213091</td>\n",
       "      <td>0.214600</td>\n",
       "      <td>0.217073</td>\n",
       "      <td>0.219041</td>\n",
       "      <td>0.222045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.992315e-03</td>\n",
       "      <td>0.019872</td>\n",
       "      <td>0.065427</td>\n",
       "      <td>0.074003</td>\n",
       "      <td>0.078344</td>\n",
       "      <td>0.084622</td>\n",
       "      <td>0.087652</td>\n",
       "      <td>0.089528</td>\n",
       "      <td>0.090835</td>\n",
       "      <td>0.091810</td>\n",
       "      <td>0.093191</td>\n",
       "      <td>0.094133</td>\n",
       "      <td>0.094825</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>0.095783</td>\n",
       "      <td>0.096133</td>\n",
       "      <td>0.096423</td>\n",
       "      <td>0.096981</td>\n",
       "      <td>0.097382</td>\n",
       "      <td>0.097921</td>\n",
       "      <td>0.098271</td>\n",
       "      <td>0.098518</td>\n",
       "      <td>0.098776</td>\n",
       "      <td>0.098957</td>\n",
       "      <td>0.099089</td>\n",
       "      <td>0.099192</td>\n",
       "      <td>0.099340</td>\n",
       "      <td>0.099442</td>\n",
       "      <td>0.099572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.991754e-03</td>\n",
       "      <td>0.019835</td>\n",
       "      <td>0.063724</td>\n",
       "      <td>0.070791</td>\n",
       "      <td>0.073846</td>\n",
       "      <td>0.077044</td>\n",
       "      <td>0.077678</td>\n",
       "      <td>0.077568</td>\n",
       "      <td>0.077168</td>\n",
       "      <td>0.076642</td>\n",
       "      <td>0.075467</td>\n",
       "      <td>0.074278</td>\n",
       "      <td>0.073140</td>\n",
       "      <td>0.072069</td>\n",
       "      <td>0.071067</td>\n",
       "      <td>7.013100e-02</td>\n",
       "      <td>6.925429e-02</td>\n",
       "      <td>6.728792e-02</td>\n",
       "      <td>6.558461e-02</td>\n",
       "      <td>6.275848e-02</td>\n",
       "      <td>6.047909e-02</td>\n",
       "      <td>5.857770e-02</td>\n",
       "      <td>5.622427e-02</td>\n",
       "      <td>5.429247e-02</td>\n",
       "      <td>5.266523e-02</td>\n",
       "      <td>5.125899e-02</td>\n",
       "      <td>4.893444e-02</td>\n",
       "      <td>4.706765e-02</td>\n",
       "      <td>4.419414e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933582</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.320007</td>\n",
       "      <td>0.267789</td>\n",
       "      <td>0.181156</td>\n",
       "      <td>0.167704</td>\n",
       "      <td>0.161147</td>\n",
       "      <td>0.152048</td>\n",
       "      <td>0.147856</td>\n",
       "      <td>0.145341</td>\n",
       "      <td>0.143635</td>\n",
       "      <td>0.142388</td>\n",
       "      <td>0.140670</td>\n",
       "      <td>0.139529</td>\n",
       "      <td>0.138709</td>\n",
       "      <td>0.138092</td>\n",
       "      <td>0.137605</td>\n",
       "      <td>0.137213</td>\n",
       "      <td>0.136887</td>\n",
       "      <td>0.136278</td>\n",
       "      <td>0.135854</td>\n",
       "      <td>0.135292</td>\n",
       "      <td>0.134940</td>\n",
       "      <td>0.134691</td>\n",
       "      <td>0.134441</td>\n",
       "      <td>0.134266</td>\n",
       "      <td>0.134143</td>\n",
       "      <td>0.134042</td>\n",
       "      <td>0.133912</td>\n",
       "      <td>0.133802</td>\n",
       "      <td>0.133703</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.486673</td>\n",
       "      <td>0.434456</td>\n",
       "      <td>0.347823</td>\n",
       "      <td>0.334371</td>\n",
       "      <td>0.327814</td>\n",
       "      <td>0.318714</td>\n",
       "      <td>0.314523</td>\n",
       "      <td>0.312008</td>\n",
       "      <td>0.310302</td>\n",
       "      <td>0.309054</td>\n",
       "      <td>0.307337</td>\n",
       "      <td>0.306196</td>\n",
       "      <td>0.305376</td>\n",
       "      <td>0.304758</td>\n",
       "      <td>0.304272</td>\n",
       "      <td>0.303880</td>\n",
       "      <td>0.303554</td>\n",
       "      <td>0.302945</td>\n",
       "      <td>0.302520</td>\n",
       "      <td>0.301959</td>\n",
       "      <td>0.301606</td>\n",
       "      <td>0.301357</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.300933</td>\n",
       "      <td>0.300810</td>\n",
       "      <td>0.300709</td>\n",
       "      <td>0.300578</td>\n",
       "      <td>0.300468</td>\n",
       "      <td>0.300370</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.993337</td>\n",
       "      <td>0.967228</td>\n",
       "      <td>0.923911</td>\n",
       "      <td>0.917186</td>\n",
       "      <td>0.913907</td>\n",
       "      <td>0.909357</td>\n",
       "      <td>0.907262</td>\n",
       "      <td>0.906004</td>\n",
       "      <td>0.905151</td>\n",
       "      <td>0.904527</td>\n",
       "      <td>0.903668</td>\n",
       "      <td>0.903098</td>\n",
       "      <td>0.902688</td>\n",
       "      <td>0.902379</td>\n",
       "      <td>0.902136</td>\n",
       "      <td>0.901940</td>\n",
       "      <td>0.901777</td>\n",
       "      <td>0.901472</td>\n",
       "      <td>0.901260</td>\n",
       "      <td>0.900979</td>\n",
       "      <td>0.900803</td>\n",
       "      <td>0.900679</td>\n",
       "      <td>0.900554</td>\n",
       "      <td>0.900466</td>\n",
       "      <td>0.900405</td>\n",
       "      <td>0.900354</td>\n",
       "      <td>0.900289</td>\n",
       "      <td>0.900234</td>\n",
       "      <td>0.900185</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.093337</td>\n",
       "      <td>0.067228</td>\n",
       "      <td>0.023911</td>\n",
       "      <td>0.017186</td>\n",
       "      <td>0.013907</td>\n",
       "      <td>0.009357</td>\n",
       "      <td>0.007262</td>\n",
       "      <td>0.006004</td>\n",
       "      <td>0.005151</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.003668</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.002688</td>\n",
       "      <td>0.002379</td>\n",
       "      <td>0.002136</td>\n",
       "      <td>0.001940</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>0.001472</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.000979</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.000679</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166927</td>\n",
       "      <td>0.176665</td>\n",
       "      <td>0.238848</td>\n",
       "      <td>0.247574</td>\n",
       "      <td>0.251461</td>\n",
       "      <td>0.256614</td>\n",
       "      <td>0.258918</td>\n",
       "      <td>0.260284</td>\n",
       "      <td>0.261204</td>\n",
       "      <td>0.261874</td>\n",
       "      <td>0.262792</td>\n",
       "      <td>0.263399</td>\n",
       "      <td>0.263835</td>\n",
       "      <td>0.264162</td>\n",
       "      <td>0.264419</td>\n",
       "      <td>0.264627</td>\n",
       "      <td>0.264799</td>\n",
       "      <td>0.265120</td>\n",
       "      <td>0.265344</td>\n",
       "      <td>0.265639</td>\n",
       "      <td>0.265824</td>\n",
       "      <td>0.265955</td>\n",
       "      <td>0.266086</td>\n",
       "      <td>0.266178</td>\n",
       "      <td>0.266242</td>\n",
       "      <td>0.266295</td>\n",
       "      <td>0.266364</td>\n",
       "      <td>0.266421</td>\n",
       "      <td>0.266473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.663411e-03</td>\n",
       "      <td>0.032772</td>\n",
       "      <td>0.076089</td>\n",
       "      <td>0.082814</td>\n",
       "      <td>0.086093</td>\n",
       "      <td>0.090643</td>\n",
       "      <td>0.092738</td>\n",
       "      <td>0.093996</td>\n",
       "      <td>0.094849</td>\n",
       "      <td>0.095473</td>\n",
       "      <td>0.096332</td>\n",
       "      <td>0.096902</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.097621</td>\n",
       "      <td>0.097864</td>\n",
       "      <td>0.098060</td>\n",
       "      <td>0.098223</td>\n",
       "      <td>0.098528</td>\n",
       "      <td>0.098740</td>\n",
       "      <td>0.099021</td>\n",
       "      <td>0.099197</td>\n",
       "      <td>0.099321</td>\n",
       "      <td>0.099446</td>\n",
       "      <td>0.099534</td>\n",
       "      <td>0.099595</td>\n",
       "      <td>0.099646</td>\n",
       "      <td>0.099711</td>\n",
       "      <td>0.099766</td>\n",
       "      <td>0.099815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.403229e-03</td>\n",
       "      <td>0.022774</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.001907</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.000696</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.000379</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.000266</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>1.001287e-04</td>\n",
       "      <td>9.097686e-05</td>\n",
       "      <td>7.424278e-05</td>\n",
       "      <td>6.288941e-05</td>\n",
       "      <td>4.819785e-05</td>\n",
       "      <td>3.918124e-05</td>\n",
       "      <td>3.289082e-05</td>\n",
       "      <td>2.668646e-05</td>\n",
       "      <td>2.236475e-05</td>\n",
       "      <td>1.936135e-05</td>\n",
       "      <td>1.689938e-05</td>\n",
       "      <td>1.375380e-05</td>\n",
       "      <td>1.109475e-05</td>\n",
       "      <td>8.746055e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933583</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.323456</td>\n",
       "      <td>0.278272</td>\n",
       "      <td>0.188772</td>\n",
       "      <td>0.173904</td>\n",
       "      <td>0.166546</td>\n",
       "      <td>0.156165</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.148334</td>\n",
       "      <td>0.146305</td>\n",
       "      <td>0.144806</td>\n",
       "      <td>0.142721</td>\n",
       "      <td>0.141322</td>\n",
       "      <td>0.140310</td>\n",
       "      <td>0.139536</td>\n",
       "      <td>0.138926</td>\n",
       "      <td>0.138432</td>\n",
       "      <td>0.138021</td>\n",
       "      <td>0.137244</td>\n",
       "      <td>0.136695</td>\n",
       "      <td>0.135968</td>\n",
       "      <td>0.135500</td>\n",
       "      <td>0.135176</td>\n",
       "      <td>0.134844</td>\n",
       "      <td>0.134610</td>\n",
       "      <td>0.134443</td>\n",
       "      <td>0.134310</td>\n",
       "      <td>0.134130</td>\n",
       "      <td>0.134002</td>\n",
       "      <td>0.133845</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.490122</td>\n",
       "      <td>0.444939</td>\n",
       "      <td>0.355439</td>\n",
       "      <td>0.340570</td>\n",
       "      <td>0.333212</td>\n",
       "      <td>0.322832</td>\n",
       "      <td>0.317959</td>\n",
       "      <td>0.315000</td>\n",
       "      <td>0.312971</td>\n",
       "      <td>0.311473</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.307988</td>\n",
       "      <td>0.306977</td>\n",
       "      <td>0.306203</td>\n",
       "      <td>0.305593</td>\n",
       "      <td>0.305099</td>\n",
       "      <td>0.304687</td>\n",
       "      <td>0.303911</td>\n",
       "      <td>0.303362</td>\n",
       "      <td>0.302634</td>\n",
       "      <td>0.302167</td>\n",
       "      <td>0.301843</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.301276</td>\n",
       "      <td>0.301110</td>\n",
       "      <td>0.300977</td>\n",
       "      <td>0.300796</td>\n",
       "      <td>0.300668</td>\n",
       "      <td>0.300512</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995061</td>\n",
       "      <td>0.972469</td>\n",
       "      <td>0.927719</td>\n",
       "      <td>0.920285</td>\n",
       "      <td>0.916606</td>\n",
       "      <td>0.911416</td>\n",
       "      <td>0.908980</td>\n",
       "      <td>0.907500</td>\n",
       "      <td>0.906486</td>\n",
       "      <td>0.905736</td>\n",
       "      <td>0.904694</td>\n",
       "      <td>0.903994</td>\n",
       "      <td>0.903489</td>\n",
       "      <td>0.903102</td>\n",
       "      <td>0.902796</td>\n",
       "      <td>0.902549</td>\n",
       "      <td>0.902344</td>\n",
       "      <td>0.901955</td>\n",
       "      <td>0.901681</td>\n",
       "      <td>0.901317</td>\n",
       "      <td>0.901083</td>\n",
       "      <td>0.900921</td>\n",
       "      <td>0.900755</td>\n",
       "      <td>0.900638</td>\n",
       "      <td>0.900555</td>\n",
       "      <td>0.900488</td>\n",
       "      <td>0.900398</td>\n",
       "      <td>0.900334</td>\n",
       "      <td>0.900256</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.095061</td>\n",
       "      <td>0.072469</td>\n",
       "      <td>0.027719</td>\n",
       "      <td>0.020285</td>\n",
       "      <td>0.016606</td>\n",
       "      <td>0.011416</td>\n",
       "      <td>0.008980</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.006486</td>\n",
       "      <td>0.005736</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.003994</td>\n",
       "      <td>0.003489</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.002796</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.001681</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.170968</td>\n",
       "      <td>0.193918</td>\n",
       "      <td>0.238919</td>\n",
       "      <td>0.246366</td>\n",
       "      <td>0.250049</td>\n",
       "      <td>0.255244</td>\n",
       "      <td>0.257682</td>\n",
       "      <td>0.259163</td>\n",
       "      <td>0.260178</td>\n",
       "      <td>0.260928</td>\n",
       "      <td>0.261971</td>\n",
       "      <td>0.262671</td>\n",
       "      <td>0.263177</td>\n",
       "      <td>0.263564</td>\n",
       "      <td>0.263869</td>\n",
       "      <td>0.264116</td>\n",
       "      <td>0.264322</td>\n",
       "      <td>0.264710</td>\n",
       "      <td>0.264985</td>\n",
       "      <td>0.265349</td>\n",
       "      <td>0.265583</td>\n",
       "      <td>0.265745</td>\n",
       "      <td>0.265911</td>\n",
       "      <td>0.266028</td>\n",
       "      <td>0.266112</td>\n",
       "      <td>0.266178</td>\n",
       "      <td>0.266268</td>\n",
       "      <td>0.266332</td>\n",
       "      <td>0.266411</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.938893e-03</td>\n",
       "      <td>0.027531</td>\n",
       "      <td>0.072281</td>\n",
       "      <td>0.079715</td>\n",
       "      <td>0.083394</td>\n",
       "      <td>0.088584</td>\n",
       "      <td>0.091020</td>\n",
       "      <td>0.092500</td>\n",
       "      <td>0.093514</td>\n",
       "      <td>0.094264</td>\n",
       "      <td>0.095306</td>\n",
       "      <td>0.096006</td>\n",
       "      <td>0.096511</td>\n",
       "      <td>0.096898</td>\n",
       "      <td>0.097204</td>\n",
       "      <td>0.097451</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>0.098045</td>\n",
       "      <td>0.098319</td>\n",
       "      <td>0.098683</td>\n",
       "      <td>0.098917</td>\n",
       "      <td>0.099079</td>\n",
       "      <td>0.099245</td>\n",
       "      <td>0.099362</td>\n",
       "      <td>0.099445</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>0.099602</td>\n",
       "      <td>0.099666</td>\n",
       "      <td>0.099744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.378167e-04</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>9.763187e-07</td>\n",
       "      <td>8.893180e-07</td>\n",
       "      <td>7.293754e-07</td>\n",
       "      <td>6.193739e-07</td>\n",
       "      <td>4.775315e-07</td>\n",
       "      <td>3.885170e-07</td>\n",
       "      <td>3.280314e-07</td>\n",
       "      <td>2.669785e-07</td>\n",
       "      <td>2.243345e-07</td>\n",
       "      <td>1.943700e-07</td>\n",
       "      <td>1.704890e-07</td>\n",
       "      <td>1.384731e-07</td>\n",
       "      <td>1.158476e-07</td>\n",
       "      <td>8.839853e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933584</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333330</td>\n",
       "      <td>0.333262</td>\n",
       "      <td>0.333191</td>\n",
       "      <td>0.333120</td>\n",
       "      <td>0.332907</td>\n",
       "      <td>0.332695</td>\n",
       "      <td>0.332484</td>\n",
       "      <td>0.332274</td>\n",
       "      <td>0.332066</td>\n",
       "      <td>0.331650</td>\n",
       "      <td>0.331239</td>\n",
       "      <td>0.330831</td>\n",
       "      <td>0.330426</td>\n",
       "      <td>0.330025</td>\n",
       "      <td>0.329627</td>\n",
       "      <td>0.329233</td>\n",
       "      <td>0.328261</td>\n",
       "      <td>0.327309</td>\n",
       "      <td>0.325463</td>\n",
       "      <td>0.323686</td>\n",
       "      <td>0.321976</td>\n",
       "      <td>0.319525</td>\n",
       "      <td>0.317201</td>\n",
       "      <td>0.314991</td>\n",
       "      <td>0.312886</td>\n",
       "      <td>0.308956</td>\n",
       "      <td>0.305354</td>\n",
       "      <td>0.298953</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.499996</td>\n",
       "      <td>0.499929</td>\n",
       "      <td>0.499857</td>\n",
       "      <td>0.499786</td>\n",
       "      <td>0.499574</td>\n",
       "      <td>0.499362</td>\n",
       "      <td>0.499151</td>\n",
       "      <td>0.498941</td>\n",
       "      <td>0.498732</td>\n",
       "      <td>0.498317</td>\n",
       "      <td>0.497905</td>\n",
       "      <td>0.497497</td>\n",
       "      <td>0.497093</td>\n",
       "      <td>0.496692</td>\n",
       "      <td>0.496294</td>\n",
       "      <td>0.495899</td>\n",
       "      <td>0.494928</td>\n",
       "      <td>0.493976</td>\n",
       "      <td>0.492129</td>\n",
       "      <td>0.490353</td>\n",
       "      <td>0.488643</td>\n",
       "      <td>0.486192</td>\n",
       "      <td>0.483868</td>\n",
       "      <td>0.481658</td>\n",
       "      <td>0.479553</td>\n",
       "      <td>0.475623</td>\n",
       "      <td>0.472021</td>\n",
       "      <td>0.465620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999964</td>\n",
       "      <td>0.999929</td>\n",
       "      <td>0.999893</td>\n",
       "      <td>0.999787</td>\n",
       "      <td>0.999681</td>\n",
       "      <td>0.999576</td>\n",
       "      <td>0.999471</td>\n",
       "      <td>0.999366</td>\n",
       "      <td>0.999159</td>\n",
       "      <td>0.998953</td>\n",
       "      <td>0.998749</td>\n",
       "      <td>0.998546</td>\n",
       "      <td>0.998346</td>\n",
       "      <td>0.998147</td>\n",
       "      <td>0.997950</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>0.996988</td>\n",
       "      <td>0.996065</td>\n",
       "      <td>0.995176</td>\n",
       "      <td>0.994321</td>\n",
       "      <td>0.993096</td>\n",
       "      <td>0.991934</td>\n",
       "      <td>0.990829</td>\n",
       "      <td>0.989776</td>\n",
       "      <td>0.987811</td>\n",
       "      <td>0.986010</td>\n",
       "      <td>0.982810</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.099998</td>\n",
       "      <td>0.099964</td>\n",
       "      <td>0.099929</td>\n",
       "      <td>0.099893</td>\n",
       "      <td>0.099787</td>\n",
       "      <td>0.099681</td>\n",
       "      <td>0.099576</td>\n",
       "      <td>0.099471</td>\n",
       "      <td>0.099366</td>\n",
       "      <td>0.099159</td>\n",
       "      <td>0.098953</td>\n",
       "      <td>0.098749</td>\n",
       "      <td>0.098546</td>\n",
       "      <td>0.098346</td>\n",
       "      <td>0.098147</td>\n",
       "      <td>0.097950</td>\n",
       "      <td>0.097464</td>\n",
       "      <td>0.096988</td>\n",
       "      <td>0.096065</td>\n",
       "      <td>0.095176</td>\n",
       "      <td>0.094321</td>\n",
       "      <td>0.093096</td>\n",
       "      <td>0.091934</td>\n",
       "      <td>0.090829</td>\n",
       "      <td>0.089776</td>\n",
       "      <td>0.087811</td>\n",
       "      <td>0.086010</td>\n",
       "      <td>0.082810</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166668</td>\n",
       "      <td>0.166669</td>\n",
       "      <td>0.166670</td>\n",
       "      <td>0.166671</td>\n",
       "      <td>0.166673</td>\n",
       "      <td>0.166675</td>\n",
       "      <td>0.166683</td>\n",
       "      <td>0.166693</td>\n",
       "      <td>0.166725</td>\n",
       "      <td>0.166775</td>\n",
       "      <td>0.166843</td>\n",
       "      <td>0.166980</td>\n",
       "      <td>0.167159</td>\n",
       "      <td>0.167377</td>\n",
       "      <td>0.167630</td>\n",
       "      <td>0.168214</td>\n",
       "      <td>0.168873</td>\n",
       "      <td>0.170282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.784502e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>0.001454</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>0.002536</td>\n",
       "      <td>0.003012</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.004824</td>\n",
       "      <td>0.005679</td>\n",
       "      <td>0.006904</td>\n",
       "      <td>0.008066</td>\n",
       "      <td>0.009171</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.012189</td>\n",
       "      <td>0.013990</td>\n",
       "      <td>0.017190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.784502e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>0.000529</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>0.001447</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>1.839982e-03</td>\n",
       "      <td>2.032685e-03</td>\n",
       "      <td>2.503605e-03</td>\n",
       "      <td>2.958680e-03</td>\n",
       "      <td>3.818451e-03</td>\n",
       "      <td>4.606387e-03</td>\n",
       "      <td>5.326639e-03</td>\n",
       "      <td>6.277075e-03</td>\n",
       "      <td>7.080558e-03</td>\n",
       "      <td>7.749471e-03</td>\n",
       "      <td>8.297305e-03</td>\n",
       "      <td>9.092941e-03</td>\n",
       "      <td>9.576317e-03</td>\n",
       "      <td>9.959288e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933585</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333332</td>\n",
       "      <td>0.333322</td>\n",
       "      <td>0.333111</td>\n",
       "      <td>0.332889</td>\n",
       "      <td>0.332668</td>\n",
       "      <td>0.332013</td>\n",
       "      <td>0.331366</td>\n",
       "      <td>0.330728</td>\n",
       "      <td>0.330098</td>\n",
       "      <td>0.329477</td>\n",
       "      <td>0.328259</td>\n",
       "      <td>0.327072</td>\n",
       "      <td>0.325914</td>\n",
       "      <td>0.324784</td>\n",
       "      <td>0.323682</td>\n",
       "      <td>0.322605</td>\n",
       "      <td>0.321553</td>\n",
       "      <td>0.319025</td>\n",
       "      <td>0.316631</td>\n",
       "      <td>0.312197</td>\n",
       "      <td>0.308170</td>\n",
       "      <td>0.304486</td>\n",
       "      <td>0.299497</td>\n",
       "      <td>0.295046</td>\n",
       "      <td>0.291030</td>\n",
       "      <td>0.287380</td>\n",
       "      <td>0.280965</td>\n",
       "      <td>0.275474</td>\n",
       "      <td>0.266462</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.499999</td>\n",
       "      <td>0.499989</td>\n",
       "      <td>0.499777</td>\n",
       "      <td>0.499556</td>\n",
       "      <td>0.499335</td>\n",
       "      <td>0.498679</td>\n",
       "      <td>0.498033</td>\n",
       "      <td>0.497395</td>\n",
       "      <td>0.496765</td>\n",
       "      <td>0.496144</td>\n",
       "      <td>0.494926</td>\n",
       "      <td>0.493738</td>\n",
       "      <td>0.492581</td>\n",
       "      <td>0.491451</td>\n",
       "      <td>0.490348</td>\n",
       "      <td>0.489272</td>\n",
       "      <td>0.488220</td>\n",
       "      <td>0.485692</td>\n",
       "      <td>0.483298</td>\n",
       "      <td>0.478864</td>\n",
       "      <td>0.474837</td>\n",
       "      <td>0.471153</td>\n",
       "      <td>0.466164</td>\n",
       "      <td>0.461712</td>\n",
       "      <td>0.457696</td>\n",
       "      <td>0.454047</td>\n",
       "      <td>0.447632</td>\n",
       "      <td>0.442141</td>\n",
       "      <td>0.433129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.999994</td>\n",
       "      <td>0.999889</td>\n",
       "      <td>0.999778</td>\n",
       "      <td>0.999668</td>\n",
       "      <td>0.999340</td>\n",
       "      <td>0.999016</td>\n",
       "      <td>0.998697</td>\n",
       "      <td>0.998383</td>\n",
       "      <td>0.998072</td>\n",
       "      <td>0.997463</td>\n",
       "      <td>0.996869</td>\n",
       "      <td>0.996290</td>\n",
       "      <td>0.995726</td>\n",
       "      <td>0.995174</td>\n",
       "      <td>0.994636</td>\n",
       "      <td>0.994110</td>\n",
       "      <td>0.992846</td>\n",
       "      <td>0.991649</td>\n",
       "      <td>0.989432</td>\n",
       "      <td>0.987418</td>\n",
       "      <td>0.985576</td>\n",
       "      <td>0.983082</td>\n",
       "      <td>0.980856</td>\n",
       "      <td>0.978848</td>\n",
       "      <td>0.977023</td>\n",
       "      <td>0.973816</td>\n",
       "      <td>0.971071</td>\n",
       "      <td>0.966565</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.099999</td>\n",
       "      <td>0.099994</td>\n",
       "      <td>0.099889</td>\n",
       "      <td>0.099778</td>\n",
       "      <td>0.099668</td>\n",
       "      <td>0.099340</td>\n",
       "      <td>0.099016</td>\n",
       "      <td>0.098697</td>\n",
       "      <td>0.098383</td>\n",
       "      <td>0.098072</td>\n",
       "      <td>0.097463</td>\n",
       "      <td>0.096869</td>\n",
       "      <td>0.096290</td>\n",
       "      <td>0.095726</td>\n",
       "      <td>0.095174</td>\n",
       "      <td>0.094636</td>\n",
       "      <td>0.094110</td>\n",
       "      <td>0.092846</td>\n",
       "      <td>0.091649</td>\n",
       "      <td>0.089432</td>\n",
       "      <td>0.087418</td>\n",
       "      <td>0.085576</td>\n",
       "      <td>0.083082</td>\n",
       "      <td>0.080856</td>\n",
       "      <td>0.078848</td>\n",
       "      <td>0.077023</td>\n",
       "      <td>0.073816</td>\n",
       "      <td>0.071071</td>\n",
       "      <td>0.066565</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166669</td>\n",
       "      <td>0.166674</td>\n",
       "      <td>0.166683</td>\n",
       "      <td>0.166697</td>\n",
       "      <td>0.166717</td>\n",
       "      <td>0.166778</td>\n",
       "      <td>0.166866</td>\n",
       "      <td>0.166983</td>\n",
       "      <td>0.167125</td>\n",
       "      <td>0.167288</td>\n",
       "      <td>0.167470</td>\n",
       "      <td>0.167667</td>\n",
       "      <td>0.168199</td>\n",
       "      <td>0.168758</td>\n",
       "      <td>0.169876</td>\n",
       "      <td>0.170934</td>\n",
       "      <td>0.171912</td>\n",
       "      <td>0.173244</td>\n",
       "      <td>0.174433</td>\n",
       "      <td>0.175503</td>\n",
       "      <td>0.176475</td>\n",
       "      <td>0.178181</td>\n",
       "      <td>0.179637</td>\n",
       "      <td>0.182023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.579526e-07</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.001928</td>\n",
       "      <td>0.002537</td>\n",
       "      <td>0.003131</td>\n",
       "      <td>0.003710</td>\n",
       "      <td>0.004274</td>\n",
       "      <td>0.004826</td>\n",
       "      <td>0.005364</td>\n",
       "      <td>0.005890</td>\n",
       "      <td>0.007154</td>\n",
       "      <td>0.008351</td>\n",
       "      <td>0.010568</td>\n",
       "      <td>0.012582</td>\n",
       "      <td>0.014424</td>\n",
       "      <td>0.016918</td>\n",
       "      <td>0.019144</td>\n",
       "      <td>0.021152</td>\n",
       "      <td>0.022977</td>\n",
       "      <td>0.026184</td>\n",
       "      <td>0.028929</td>\n",
       "      <td>0.033435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.579526e-07</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>0.000969</td>\n",
       "      <td>0.001270</td>\n",
       "      <td>0.001557</td>\n",
       "      <td>0.001828</td>\n",
       "      <td>0.002314</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.003077</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>3.756540e-03</td>\n",
       "      <td>3.889379e-03</td>\n",
       "      <td>4.089237e-03</td>\n",
       "      <td>4.167998e-03</td>\n",
       "      <td>4.149791e-03</td>\n",
       "      <td>4.047360e-03</td>\n",
       "      <td>3.932019e-03</td>\n",
       "      <td>3.763374e-03</td>\n",
       "      <td>3.611976e-03</td>\n",
       "      <td>3.478599e-03</td>\n",
       "      <td>3.359512e-03</td>\n",
       "      <td>3.156144e-03</td>\n",
       "      <td>2.987952e-03</td>\n",
       "      <td>2.723336e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1933581  0.333333  0.327349  0.293590  0.202480  0.185327  0.176645  0.164088   \n",
       "1933582  0.333333  0.320007  0.267789  0.181156  0.167704  0.161147  0.152048   \n",
       "1933583  0.333333  0.323456  0.278272  0.188772  0.173904  0.166546  0.156165   \n",
       "1933584  0.333333  0.333333  0.333330  0.333262  0.333191  0.333120  0.332907   \n",
       "1933585  0.333333  0.333332  0.333322  0.333111  0.332889  0.332668  0.332013   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  cSM_360s  cSM_480s  cSM_600s  cSM_720s  \\\n",
       "1933581  0.158029  0.154277  0.151664  0.149713  0.146951  0.145067  0.143684   \n",
       "1933582  0.147856  0.145341  0.143635  0.142388  0.140670  0.139529  0.138709   \n",
       "1933583  0.151293  0.148334  0.146305  0.144806  0.142721  0.141322  0.140310   \n",
       "1933584  0.332695  0.332484  0.332274  0.332066  0.331650  0.331239  0.330831   \n",
       "1933585  0.331366  0.330728  0.330098  0.329477  0.328259  0.327072  0.325914   \n",
       "\n",
       "         cSM_840s  cSM_960s  cSM_1080s  cSM_1200s  cSM_1500s  cSM_1800s  \\\n",
       "1933581  0.142618  0.141768   0.141068   0.140488   0.139372   0.138569   \n",
       "1933582  0.138092  0.137605   0.137213   0.136887   0.136278   0.135854   \n",
       "1933583  0.139536  0.138926   0.138432   0.138021   0.137244   0.136695   \n",
       "1933584  0.330426  0.330025   0.329627   0.329233   0.328261   0.327309   \n",
       "1933585  0.324784  0.323682   0.322605   0.321553   0.319025   0.316631   \n",
       "\n",
       "         cSM_2400s  cSM_3000s  cSM_3600s  cSM_4500s  cSM_5400s  cSM_6300s  \\\n",
       "1933581   0.137491   0.136792   0.136298   0.135781   0.135420   0.135155   \n",
       "1933582   0.135292   0.134940   0.134691   0.134441   0.134266   0.134143   \n",
       "1933583   0.135968   0.135500   0.135176   0.134844   0.134610   0.134443   \n",
       "1933584   0.325463   0.323686   0.321976   0.319525   0.317201   0.314991   \n",
       "1933585   0.312197   0.308170   0.304486   0.299497   0.295046   0.291030   \n",
       "\n",
       "         cSM_7200s  cSM_9000s  cSM_10800s  cSM_14400s  cR_0s   cR_0.1s  \\\n",
       "1933581   0.134949   0.134653    0.134450    0.134189    0.5  0.494015   \n",
       "1933582   0.134042   0.133912    0.133802    0.133703    0.5  0.486673   \n",
       "1933583   0.134310   0.134130    0.134002    0.133845    0.5  0.490122   \n",
       "1933584   0.312886   0.308956    0.305354    0.298953    0.5  0.500000   \n",
       "1933585   0.287380   0.280965    0.275474    0.266462    0.5  0.499999   \n",
       "\n",
       "            cR_1s    cR_20s    cR_40s    cR_60s   cR_120s   cR_180s   cR_240s  \\\n",
       "1933581  0.460257  0.369147  0.351994  0.343312  0.330755  0.324695  0.320944   \n",
       "1933582  0.434456  0.347823  0.334371  0.327814  0.318714  0.314523  0.312008   \n",
       "1933583  0.444939  0.355439  0.340570  0.333212  0.322832  0.317959  0.315000   \n",
       "1933584  0.499996  0.499929  0.499857  0.499786  0.499574  0.499362  0.499151   \n",
       "1933585  0.499989  0.499777  0.499556  0.499335  0.498679  0.498033  0.497395   \n",
       "\n",
       "          cR_300s   cR_360s   cR_480s   cR_600s   cR_720s   cR_840s   cR_960s  \\\n",
       "1933581  0.318331  0.316380  0.313618  0.311734  0.310350  0.309285  0.308435   \n",
       "1933582  0.310302  0.309054  0.307337  0.306196  0.305376  0.304758  0.304272   \n",
       "1933583  0.312971  0.311473  0.309388  0.307988  0.306977  0.306203  0.305593   \n",
       "1933584  0.498941  0.498732  0.498317  0.497905  0.497497  0.497093  0.496692   \n",
       "1933585  0.496765  0.496144  0.494926  0.493738  0.492581  0.491451  0.490348   \n",
       "\n",
       "         cR_1080s  cR_1200s  cR_1500s  cR_1800s  cR_2400s  cR_3000s  cR_3600s  \\\n",
       "1933581  0.307735  0.307155  0.306038  0.305235  0.304157  0.303458  0.302965   \n",
       "1933582  0.303880  0.303554  0.302945  0.302520  0.301959  0.301606  0.301357   \n",
       "1933583  0.305099  0.304687  0.303911  0.303362  0.302634  0.302167  0.301843   \n",
       "1933584  0.496294  0.495899  0.494928  0.493976  0.492129  0.490353  0.488643   \n",
       "1933585  0.489272  0.488220  0.485692  0.483298  0.478864  0.474837  0.471153   \n",
       "\n",
       "         cR_4500s  cR_5400s  cR_6300s  cR_7200s  cR_9000s  cR_10800s  \\\n",
       "1933581  0.302447  0.302086  0.301821  0.301615  0.301319   0.301117   \n",
       "1933582  0.301108  0.300933  0.300810  0.300709  0.300578   0.300468   \n",
       "1933583  0.301511  0.301276  0.301110  0.300977  0.300796   0.300668   \n",
       "1933584  0.486192  0.483868  0.481658  0.479553  0.475623   0.472021   \n",
       "1933585  0.466164  0.461712  0.457696  0.454047  0.447632   0.442141   \n",
       "\n",
       "         cR_14400s  cB_0s   cB_0.1s     cB_1s    cB_20s    cB_40s    cB_60s  \\\n",
       "1933581   0.300856    1.0  0.997008  0.980128  0.934573  0.925997  0.921656   \n",
       "1933582   0.300370    1.0  0.993337  0.967228  0.923911  0.917186  0.913907   \n",
       "1933583   0.300512    1.0  0.995061  0.972469  0.927719  0.920285  0.916606   \n",
       "1933584   0.465620    1.0  1.000000  0.999998  0.999964  0.999929  0.999893   \n",
       "1933585   0.433129    1.0  0.999999  0.999994  0.999889  0.999778  0.999668   \n",
       "\n",
       "          cB_120s   cB_180s   cB_240s   cB_300s   cB_360s   cB_480s   cB_600s  \\\n",
       "1933581  0.915378  0.912348  0.910472  0.909165  0.908190  0.906809  0.905867   \n",
       "1933582  0.909357  0.907262  0.906004  0.905151  0.904527  0.903668  0.903098   \n",
       "1933583  0.911416  0.908980  0.907500  0.906486  0.905736  0.904694  0.903994   \n",
       "1933584  0.999787  0.999681  0.999576  0.999471  0.999366  0.999159  0.998953   \n",
       "1933585  0.999340  0.999016  0.998697  0.998383  0.998072  0.997463  0.996869   \n",
       "\n",
       "          cB_720s   cB_840s   cB_960s  cB_1080s  cB_1200s  cB_1500s  cB_1800s  \\\n",
       "1933581  0.905175  0.904642  0.904217  0.903867  0.903577  0.903019  0.902618   \n",
       "1933582  0.902688  0.902379  0.902136  0.901940  0.901777  0.901472  0.901260   \n",
       "1933583  0.903489  0.903102  0.902796  0.902549  0.902344  0.901955  0.901681   \n",
       "1933584  0.998749  0.998546  0.998346  0.998147  0.997950  0.997464  0.996988   \n",
       "1933585  0.996290  0.995726  0.995174  0.994636  0.994110  0.992846  0.991649   \n",
       "\n",
       "         cB_2400s  cB_3000s  cB_3600s  cB_4500s  cB_5400s  cB_6300s  cB_7200s  \\\n",
       "1933581  0.902079  0.901729  0.901482  0.901224  0.901043  0.900911  0.900808   \n",
       "1933582  0.900979  0.900803  0.900679  0.900554  0.900466  0.900405  0.900354   \n",
       "1933583  0.901317  0.901083  0.900921  0.900755  0.900638  0.900555  0.900488   \n",
       "1933584  0.996065  0.995176  0.994321  0.993096  0.991934  0.990829  0.989776   \n",
       "1933585  0.989432  0.987418  0.985576  0.983082  0.980856  0.978848  0.977023   \n",
       "\n",
       "         cB_9000s  cB_10800s  cB_14400s  cC_0s   cC_0.1s     cC_1s    cC_20s  \\\n",
       "1933581  0.900660   0.900558   0.900428    0.1  0.097008  0.080128  0.034573   \n",
       "1933582  0.900289   0.900234   0.900185    0.1  0.093337  0.067228  0.023911   \n",
       "1933583  0.900398   0.900334   0.900256    0.1  0.095061  0.072469  0.027719   \n",
       "1933584  0.987811   0.986010   0.982810    0.1  0.100000  0.099998  0.099964   \n",
       "1933585  0.973816   0.971071   0.966565    0.1  0.099999  0.099994  0.099889   \n",
       "\n",
       "           cC_40s    cC_60s   cC_120s   cC_180s   cC_240s   cC_300s   cC_360s  \\\n",
       "1933581  0.025997  0.021656  0.015378  0.012348  0.010472  0.009165  0.008190   \n",
       "1933582  0.017186  0.013907  0.009357  0.007262  0.006004  0.005151  0.004527   \n",
       "1933583  0.020285  0.016606  0.011416  0.008980  0.007500  0.006486  0.005736   \n",
       "1933584  0.099929  0.099893  0.099787  0.099681  0.099576  0.099471  0.099366   \n",
       "1933585  0.099778  0.099668  0.099340  0.099016  0.098697  0.098383  0.098072   \n",
       "\n",
       "          cC_480s   cC_600s   cC_720s   cC_840s   cC_960s  cC_1080s  cC_1200s  \\\n",
       "1933581  0.006809  0.005867  0.005175  0.004642  0.004217  0.003867  0.003577   \n",
       "1933582  0.003668  0.003098  0.002688  0.002379  0.002136  0.001940  0.001777   \n",
       "1933583  0.004694  0.003994  0.003489  0.003102  0.002796  0.002549  0.002344   \n",
       "1933584  0.099159  0.098953  0.098749  0.098546  0.098346  0.098147  0.097950   \n",
       "1933585  0.097463  0.096869  0.096290  0.095726  0.095174  0.094636  0.094110   \n",
       "\n",
       "         cC_1500s  cC_1800s  cC_2400s  cC_3000s  cC_3600s  cC_4500s  cC_5400s  \\\n",
       "1933581  0.003019  0.002618  0.002079  0.001729  0.001482  0.001224  0.001043   \n",
       "1933582  0.001472  0.001260  0.000979  0.000803  0.000679  0.000554  0.000466   \n",
       "1933583  0.001955  0.001681  0.001317  0.001083  0.000921  0.000755  0.000638   \n",
       "1933584  0.097464  0.096988  0.096065  0.095176  0.094321  0.093096  0.091934   \n",
       "1933585  0.092846  0.091649  0.089432  0.087418  0.085576  0.083082  0.080856   \n",
       "\n",
       "         cC_6300s  cC_7200s  cC_9000s  cC_10800s  cC_14400s     cP_0s  \\\n",
       "1933581  0.000911  0.000808  0.000660   0.000558   0.000428  0.166667   \n",
       "1933582  0.000405  0.000354  0.000289   0.000234   0.000185  0.166667   \n",
       "1933583  0.000555  0.000488  0.000398   0.000334   0.000256  0.166667   \n",
       "1933584  0.090829  0.089776  0.087811   0.086010   0.082810  0.166667   \n",
       "1933585  0.078848  0.077023  0.073816   0.071071   0.066565  0.166667   \n",
       "\n",
       "          cP_0.1s     cP_1s    cP_20s    cP_40s    cP_60s   cP_120s   cP_180s  \\\n",
       "1933581  0.166667  0.166703  0.168370  0.169879  0.171165  0.174245  0.176641   \n",
       "1933582  0.166927  0.176665  0.238848  0.247574  0.251461  0.256614  0.258918   \n",
       "1933583  0.170968  0.193918  0.238919  0.246366  0.250049  0.255244  0.257682   \n",
       "1933584  0.166667  0.166667  0.166667  0.166667  0.166667  0.166667  0.166667   \n",
       "1933585  0.166667  0.166667  0.166667  0.166667  0.166667  0.166669  0.166674   \n",
       "\n",
       "          cP_240s   cP_300s   cP_360s   cP_480s   cP_600s   cP_720s   cP_840s  \\\n",
       "1933581  0.178627  0.180334  0.181834  0.184390  0.186522  0.188352  0.189955   \n",
       "1933582  0.260284  0.261204  0.261874  0.262792  0.263399  0.263835  0.264162   \n",
       "1933583  0.259163  0.260178  0.260928  0.261971  0.262671  0.263177  0.263564   \n",
       "1933584  0.166667  0.166667  0.166667  0.166667  0.166668  0.166669  0.166670   \n",
       "1933585  0.166683  0.166697  0.166717  0.166778  0.166866  0.166983  0.167125   \n",
       "\n",
       "          cP_960s  cP_1080s  cP_1200s  cP_1500s  cP_1800s  cP_2400s  cP_3000s  \\\n",
       "1933581  0.191382  0.192668  0.193835  0.196360  0.198464  0.201830  0.204458   \n",
       "1933582  0.264419  0.264627  0.264799  0.265120  0.265344  0.265639  0.265824   \n",
       "1933583  0.263869  0.264116  0.264322  0.264710  0.264985  0.265349  0.265583   \n",
       "1933584  0.166671  0.166673  0.166675  0.166683  0.166693  0.166725  0.166775   \n",
       "1933585  0.167288  0.167470  0.167667  0.168199  0.168758  0.169876  0.170934   \n",
       "\n",
       "         cP_3600s  cP_4500s  cP_5400s  cP_6300s  cP_7200s  cP_9000s  \\\n",
       "1933581  0.206607  0.209219  0.211331  0.213091  0.214600  0.217073   \n",
       "1933582  0.265955  0.266086  0.266178  0.266242  0.266295  0.266364   \n",
       "1933583  0.265745  0.265911  0.266028  0.266112  0.266178  0.266268   \n",
       "1933584  0.166843  0.166980  0.167159  0.167377  0.167630  0.168214   \n",
       "1933585  0.171912  0.173244  0.174433  0.175503  0.176475  0.178181   \n",
       "\n",
       "         cP_10800s  cP_14400s  cIMP1_0s    cIMP1_0.1s  cIMP1_1s  cIMP1_20s  \\\n",
       "1933581   0.219041   0.222045       0.0  2.992315e-03  0.019872   0.065427   \n",
       "1933582   0.266421   0.266473       0.0  6.663411e-03  0.032772   0.076089   \n",
       "1933583   0.266332   0.266411       0.0  4.938893e-03  0.027531   0.072281   \n",
       "1933584   0.168873   0.170282       0.0  1.784502e-07  0.000002   0.000036   \n",
       "1933585   0.179637   0.182023       0.0  5.579526e-07  0.000006   0.000111   \n",
       "\n",
       "         cIMP1_40s  cIMP1_60s  cIMP1_120s  cIMP1_180s  cIMP1_240s  cIMP1_300s  \\\n",
       "1933581   0.074003   0.078344    0.084622    0.087652    0.089528    0.090835   \n",
       "1933582   0.082814   0.086093    0.090643    0.092738    0.093996    0.094849   \n",
       "1933583   0.079715   0.083394    0.088584    0.091020    0.092500    0.093514   \n",
       "1933584   0.000071   0.000107    0.000213    0.000319    0.000424    0.000529   \n",
       "1933585   0.000222   0.000332    0.000660    0.000984    0.001303    0.001617   \n",
       "\n",
       "         cIMP1_360s  cIMP1_480s  cIMP1_600s  cIMP1_720s  cIMP1_840s  \\\n",
       "1933581    0.091810    0.093191    0.094133    0.094825    0.095358   \n",
       "1933582    0.095473    0.096332    0.096902    0.097312    0.097621   \n",
       "1933583    0.094264    0.095306    0.096006    0.096511    0.096898   \n",
       "1933584    0.000634    0.000841    0.001047    0.001251    0.001454   \n",
       "1933585    0.001928    0.002537    0.003131    0.003710    0.004274   \n",
       "\n",
       "         cIMP1_960s  cIMP1_1080s  cIMP1_1200s  cIMP1_1500s  cIMP1_1800s  \\\n",
       "1933581    0.095783     0.096133     0.096423     0.096981     0.097382   \n",
       "1933582    0.097864     0.098060     0.098223     0.098528     0.098740   \n",
       "1933583    0.097204     0.097451     0.097656     0.098045     0.098319   \n",
       "1933584    0.001654     0.001853     0.002050     0.002536     0.003012   \n",
       "1933585    0.004826     0.005364     0.005890     0.007154     0.008351   \n",
       "\n",
       "         cIMP1_2400s  cIMP1_3000s  cIMP1_3600s  cIMP1_4500s  cIMP1_5400s  \\\n",
       "1933581     0.097921     0.098271     0.098518     0.098776     0.098957   \n",
       "1933582     0.099021     0.099197     0.099321     0.099446     0.099534   \n",
       "1933583     0.098683     0.098917     0.099079     0.099245     0.099362   \n",
       "1933584     0.003935     0.004824     0.005679     0.006904     0.008066   \n",
       "1933585     0.010568     0.012582     0.014424     0.016918     0.019144   \n",
       "\n",
       "         cIMP1_6300s  cIMP1_7200s  cIMP1_9000s  cIMP1_10800s  cIMP1_14400s  \\\n",
       "1933581     0.099089     0.099192     0.099340      0.099442      0.099572   \n",
       "1933582     0.099595     0.099646     0.099711      0.099766      0.099815   \n",
       "1933583     0.099445     0.099512     0.099602      0.099666      0.099744   \n",
       "1933584     0.009171     0.010224     0.012189      0.013990      0.017190   \n",
       "1933585     0.021152     0.022977     0.026184      0.028929      0.033435   \n",
       "\n",
       "         cIMP2_0s  cIMP2_0.1s  cIMP2_1s  cIMP2_20s  cIMP2_40s  cIMP2_60s  \\\n",
       "1933581       0.0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933582       0.0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933583       0.0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933584       0.0         0.0       0.0        0.0        0.0        0.0   \n",
       "1933585       0.0         0.0       0.0        0.0        0.0        0.0   \n",
       "\n",
       "         cIMP2_120s  cIMP2_180s  cIMP2_240s  cIMP2_300s  cIMP2_360s  \\\n",
       "1933581         0.0         0.0         0.0         0.0         0.0   \n",
       "1933582         0.0         0.0         0.0         0.0         0.0   \n",
       "1933583         0.0         0.0         0.0         0.0         0.0   \n",
       "1933584         0.0         0.0         0.0         0.0         0.0   \n",
       "1933585         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "         cIMP2_480s  cIMP2_600s  cIMP2_720s  cIMP2_840s  cIMP2_960s  \\\n",
       "1933581         0.0         0.0         0.0         0.0         0.0   \n",
       "1933582         0.0         0.0         0.0         0.0         0.0   \n",
       "1933583         0.0         0.0         0.0         0.0         0.0   \n",
       "1933584         0.0         0.0         0.0         0.0         0.0   \n",
       "1933585         0.0         0.0         0.0         0.0         0.0   \n",
       "\n",
       "         cIMP2_1080s  cIMP2_1200s  cIMP2_1500s  cIMP2_1800s  cIMP2_2400s  \\\n",
       "1933581          0.0          0.0          0.0          0.0          0.0   \n",
       "1933582          0.0          0.0          0.0          0.0          0.0   \n",
       "1933583          0.0          0.0          0.0          0.0          0.0   \n",
       "1933584          0.0          0.0          0.0          0.0          0.0   \n",
       "1933585          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cIMP2_3000s  cIMP2_3600s  cIMP2_4500s  cIMP2_5400s  cIMP2_6300s  \\\n",
       "1933581          0.0          0.0          0.0          0.0          0.0   \n",
       "1933582          0.0          0.0          0.0          0.0          0.0   \n",
       "1933583          0.0          0.0          0.0          0.0          0.0   \n",
       "1933584          0.0          0.0          0.0          0.0          0.0   \n",
       "1933585          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "         cIMP2_7200s  cIMP2_9000s  cIMP2_10800s  cIMP2_14400s  cINT1_0s  \\\n",
       "1933581          0.0          0.0           0.0           0.0       0.0   \n",
       "1933582          0.0          0.0           0.0           0.0       0.0   \n",
       "1933583          0.0          0.0           0.0           0.0       0.0   \n",
       "1933584          0.0          0.0           0.0           0.0       0.0   \n",
       "1933585          0.0          0.0           0.0           0.0       0.0   \n",
       "\n",
       "           cINT1_0.1s  cINT1_1s  cINT1_20s  cINT1_40s  cINT1_60s  cINT1_120s  \\\n",
       "1933581  2.991754e-03  0.019835   0.063724   0.070791   0.073846    0.077044   \n",
       "1933582  6.403229e-03  0.022774   0.003907   0.001907   0.001299    0.000696   \n",
       "1933583  6.378167e-04  0.000280   0.000028   0.000016   0.000011    0.000006   \n",
       "1933584  1.784502e-07  0.000002   0.000036   0.000071   0.000107    0.000213   \n",
       "1933585  5.579526e-07  0.000006   0.000111   0.000222   0.000332    0.000656   \n",
       "\n",
       "         cINT1_180s  cINT1_240s  cINT1_300s  cINT1_360s  cINT1_480s  \\\n",
       "1933581    0.077678    0.077568    0.077168    0.076642    0.075467   \n",
       "1933582    0.000487    0.000379    0.000311    0.000266    0.000206   \n",
       "1933583    0.000005    0.000004    0.000003    0.000003    0.000002   \n",
       "1933584    0.000319    0.000424    0.000529    0.000633    0.000840   \n",
       "1933585    0.000969    0.001270    0.001557    0.001828    0.002314   \n",
       "\n",
       "         cINT1_600s  cINT1_720s  cINT1_840s  cINT1_960s   cINT1_1080s  \\\n",
       "1933581    0.074278    0.073140    0.072069    0.071067  7.013100e-02   \n",
       "1933582    0.000169    0.000144    0.000126    0.000111  1.001287e-04   \n",
       "1933583    0.000002    0.000001    0.000001    0.000001  9.763187e-07   \n",
       "1933584    0.001045    0.001247    0.001447    0.001645  1.839982e-03   \n",
       "1933585    0.002733    0.003077    0.003359    0.003583  3.756540e-03   \n",
       "\n",
       "          cINT1_1200s   cINT1_1500s   cINT1_1800s   cINT1_2400s   cINT1_3000s  \\\n",
       "1933581  6.925429e-02  6.728792e-02  6.558461e-02  6.275848e-02  6.047909e-02   \n",
       "1933582  9.097686e-05  7.424278e-05  6.288941e-05  4.819785e-05  3.918124e-05   \n",
       "1933583  8.893180e-07  7.293754e-07  6.193739e-07  4.775315e-07  3.885170e-07   \n",
       "1933584  2.032685e-03  2.503605e-03  2.958680e-03  3.818451e-03  4.606387e-03   \n",
       "1933585  3.889379e-03  4.089237e-03  4.167998e-03  4.149791e-03  4.047360e-03   \n",
       "\n",
       "          cINT1_3600s   cINT1_4500s   cINT1_5400s   cINT1_6300s   cINT1_7200s  \\\n",
       "1933581  5.857770e-02  5.622427e-02  5.429247e-02  5.266523e-02  5.125899e-02   \n",
       "1933582  3.289082e-05  2.668646e-05  2.236475e-05  1.936135e-05  1.689938e-05   \n",
       "1933583  3.280314e-07  2.669785e-07  2.243345e-07  1.943700e-07  1.704890e-07   \n",
       "1933584  5.326639e-03  6.277075e-03  7.080558e-03  7.749471e-03  8.297305e-03   \n",
       "1933585  3.932019e-03  3.763374e-03  3.611976e-03  3.478599e-03  3.359512e-03   \n",
       "\n",
       "          cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1933581  4.893444e-02  4.706765e-02  4.419414e-02  \n",
       "1933582  1.375380e-05  1.109475e-05  8.746055e-06  \n",
       "1933583  1.384731e-07  1.158476e-07  8.839853e-08  \n",
       "1933584  9.092941e-03  9.576317e-03  9.959288e-03  \n",
       "1933585  3.156144e-03  2.987952e-03  2.723336e-03  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 240)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mechanism_(0, 146)</th>\n",
       "      <th>Mechanism_(0, 147)</th>\n",
       "      <th>Mechanism_(0, 154)</th>\n",
       "      <th>Mechanism_(0, 155)</th>\n",
       "      <th>Mechanism_(1, 146)</th>\n",
       "      <th>Mechanism_(1, 147)</th>\n",
       "      <th>Mechanism_(1, 154)</th>\n",
       "      <th>Mechanism_(1, 155)</th>\n",
       "      <th>Mechanism_(101, 126)</th>\n",
       "      <th>Mechanism_(101, 162)</th>\n",
       "      <th>Mechanism_(101, 163)</th>\n",
       "      <th>Mechanism_(101, 179)</th>\n",
       "      <th>Mechanism_(101, 216)</th>\n",
       "      <th>Mechanism_(101, 217)</th>\n",
       "      <th>Mechanism_(101, 232)</th>\n",
       "      <th>Mechanism_(101, 270)</th>\n",
       "      <th>Mechanism_(101, 271)</th>\n",
       "      <th>Mechanism_(101, 285)</th>\n",
       "      <th>Mechanism_(101, 324)</th>\n",
       "      <th>Mechanism_(101, 325)</th>\n",
       "      <th>Mechanism_(101, 334)</th>\n",
       "      <th>Mechanism_(101, 382)</th>\n",
       "      <th>Mechanism_(101, 400)</th>\n",
       "      <th>Mechanism_(101, 448)</th>\n",
       "      <th>Mechanism_(101, 466)</th>\n",
       "      <th>Mechanism_(101, 514)</th>\n",
       "      <th>Mechanism_(101, 532)</th>\n",
       "      <th>Mechanism_(101, 580)</th>\n",
       "      <th>Mechanism_(104, 142)</th>\n",
       "      <th>Mechanism_(104, 174)</th>\n",
       "      <th>Mechanism_(104, 196)</th>\n",
       "      <th>Mechanism_(104, 228)</th>\n",
       "      <th>Mechanism_(105, 142)</th>\n",
       "      <th>Mechanism_(105, 143)</th>\n",
       "      <th>Mechanism_(105, 174)</th>\n",
       "      <th>Mechanism_(105, 175)</th>\n",
       "      <th>Mechanism_(105, 196)</th>\n",
       "      <th>Mechanism_(105, 197)</th>\n",
       "      <th>Mechanism_(105, 228)</th>\n",
       "      <th>Mechanism_(105, 229)</th>\n",
       "      <th>Mechanism_(108, 138)</th>\n",
       "      <th>Mechanism_(108, 170)</th>\n",
       "      <th>Mechanism_(108, 192)</th>\n",
       "      <th>Mechanism_(108, 224)</th>\n",
       "      <th>Mechanism_(108, 246)</th>\n",
       "      <th>Mechanism_(108, 278)</th>\n",
       "      <th>Mechanism_(108, 300)</th>\n",
       "      <th>Mechanism_(108, 332)</th>\n",
       "      <th>Mechanism_(109, 138)</th>\n",
       "      <th>Mechanism_(109, 139)</th>\n",
       "      <th>Mechanism_(109, 170)</th>\n",
       "      <th>Mechanism_(109, 171)</th>\n",
       "      <th>Mechanism_(109, 192)</th>\n",
       "      <th>Mechanism_(109, 193)</th>\n",
       "      <th>Mechanism_(109, 224)</th>\n",
       "      <th>Mechanism_(109, 225)</th>\n",
       "      <th>Mechanism_(109, 246)</th>\n",
       "      <th>Mechanism_(109, 247)</th>\n",
       "      <th>Mechanism_(109, 278)</th>\n",
       "      <th>Mechanism_(109, 279)</th>\n",
       "      <th>Mechanism_(109, 300)</th>\n",
       "      <th>Mechanism_(109, 301)</th>\n",
       "      <th>Mechanism_(109, 332)</th>\n",
       "      <th>Mechanism_(109, 333)</th>\n",
       "      <th>Mechanism_(11, 126)</th>\n",
       "      <th>Mechanism_(11, 162)</th>\n",
       "      <th>Mechanism_(11, 163)</th>\n",
       "      <th>Mechanism_(11, 334)</th>\n",
       "      <th>Mechanism_(11, 382)</th>\n",
       "      <th>Mechanism_(113, 128)</th>\n",
       "      <th>Mechanism_(113, 166)</th>\n",
       "      <th>Mechanism_(113, 167)</th>\n",
       "      <th>Mechanism_(113, 181)</th>\n",
       "      <th>Mechanism_(113, 220)</th>\n",
       "      <th>Mechanism_(113, 221)</th>\n",
       "      <th>Mechanism_(113, 338)</th>\n",
       "      <th>Mechanism_(113, 386)</th>\n",
       "      <th>Mechanism_(113, 404)</th>\n",
       "      <th>Mechanism_(113, 452)</th>\n",
       "      <th>Mechanism_(117, 126)</th>\n",
       "      <th>Mechanism_(117, 162)</th>\n",
       "      <th>Mechanism_(117, 163)</th>\n",
       "      <th>Mechanism_(117, 179)</th>\n",
       "      <th>Mechanism_(117, 216)</th>\n",
       "      <th>Mechanism_(117, 217)</th>\n",
       "      <th>Mechanism_(117, 232)</th>\n",
       "      <th>Mechanism_(117, 270)</th>\n",
       "      <th>Mechanism_(117, 271)</th>\n",
       "      <th>Mechanism_(117, 285)</th>\n",
       "      <th>Mechanism_(117, 324)</th>\n",
       "      <th>Mechanism_(117, 325)</th>\n",
       "      <th>Mechanism_(117, 334)</th>\n",
       "      <th>Mechanism_(117, 382)</th>\n",
       "      <th>Mechanism_(117, 400)</th>\n",
       "      <th>Mechanism_(117, 448)</th>\n",
       "      <th>Mechanism_(117, 466)</th>\n",
       "      <th>Mechanism_(117, 514)</th>\n",
       "      <th>Mechanism_(117, 532)</th>\n",
       "      <th>Mechanism_(117, 580)</th>\n",
       "      <th>Mechanism_(12, 138)</th>\n",
       "      <th>Mechanism_(12, 170)</th>\n",
       "      <th>Mechanism_(120, 142)</th>\n",
       "      <th>Mechanism_(120, 174)</th>\n",
       "      <th>Mechanism_(120, 196)</th>\n",
       "      <th>Mechanism_(120, 228)</th>\n",
       "      <th>Mechanism_(121, 142)</th>\n",
       "      <th>Mechanism_(121, 143)</th>\n",
       "      <th>Mechanism_(121, 174)</th>\n",
       "      <th>Mechanism_(121, 175)</th>\n",
       "      <th>Mechanism_(121, 196)</th>\n",
       "      <th>Mechanism_(121, 197)</th>\n",
       "      <th>Mechanism_(121, 228)</th>\n",
       "      <th>Mechanism_(121, 229)</th>\n",
       "      <th>Mechanism_(124, 138)</th>\n",
       "      <th>Mechanism_(124, 170)</th>\n",
       "      <th>Mechanism_(124, 192)</th>\n",
       "      <th>Mechanism_(124, 224)</th>\n",
       "      <th>Mechanism_(124, 246)</th>\n",
       "      <th>Mechanism_(124, 278)</th>\n",
       "      <th>Mechanism_(124, 300)</th>\n",
       "      <th>Mechanism_(124, 332)</th>\n",
       "      <th>Mechanism_(125, 138)</th>\n",
       "      <th>Mechanism_(125, 139)</th>\n",
       "      <th>Mechanism_(125, 170)</th>\n",
       "      <th>Mechanism_(125, 171)</th>\n",
       "      <th>Mechanism_(125, 192)</th>\n",
       "      <th>Mechanism_(125, 193)</th>\n",
       "      <th>Mechanism_(125, 224)</th>\n",
       "      <th>Mechanism_(125, 225)</th>\n",
       "      <th>Mechanism_(125, 246)</th>\n",
       "      <th>Mechanism_(125, 247)</th>\n",
       "      <th>Mechanism_(125, 278)</th>\n",
       "      <th>Mechanism_(125, 279)</th>\n",
       "      <th>Mechanism_(125, 300)</th>\n",
       "      <th>Mechanism_(125, 301)</th>\n",
       "      <th>Mechanism_(125, 332)</th>\n",
       "      <th>Mechanism_(125, 333)</th>\n",
       "      <th>Mechanism_(13, 138)</th>\n",
       "      <th>Mechanism_(13, 139)</th>\n",
       "      <th>Mechanism_(13, 170)</th>\n",
       "      <th>Mechanism_(13, 171)</th>\n",
       "      <th>Mechanism_(16, 146)</th>\n",
       "      <th>Mechanism_(16, 147)</th>\n",
       "      <th>Mechanism_(16, 154)</th>\n",
       "      <th>Mechanism_(16, 155)</th>\n",
       "      <th>Mechanism_(16, 200)</th>\n",
       "      <th>Mechanism_(16, 201)</th>\n",
       "      <th>Mechanism_(16, 208)</th>\n",
       "      <th>Mechanism_(16, 209)</th>\n",
       "      <th>Mechanism_(17, 146)</th>\n",
       "      <th>Mechanism_(17, 147)</th>\n",
       "      <th>Mechanism_(17, 154)</th>\n",
       "      <th>Mechanism_(17, 155)</th>\n",
       "      <th>Mechanism_(17, 200)</th>\n",
       "      <th>Mechanism_(17, 201)</th>\n",
       "      <th>Mechanism_(17, 208)</th>\n",
       "      <th>Mechanism_(17, 209)</th>\n",
       "      <th>Mechanism_(24, 146)</th>\n",
       "      <th>Mechanism_(24, 147)</th>\n",
       "      <th>Mechanism_(24, 154)</th>\n",
       "      <th>Mechanism_(24, 155)</th>\n",
       "      <th>Mechanism_(24, 200)</th>\n",
       "      <th>Mechanism_(24, 201)</th>\n",
       "      <th>Mechanism_(24, 208)</th>\n",
       "      <th>Mechanism_(24, 209)</th>\n",
       "      <th>Mechanism_(25, 146)</th>\n",
       "      <th>Mechanism_(25, 147)</th>\n",
       "      <th>Mechanism_(25, 154)</th>\n",
       "      <th>Mechanism_(25, 155)</th>\n",
       "      <th>Mechanism_(25, 200)</th>\n",
       "      <th>Mechanism_(25, 201)</th>\n",
       "      <th>Mechanism_(25, 208)</th>\n",
       "      <th>Mechanism_(25, 209)</th>\n",
       "      <th>Mechanism_(29, 126)</th>\n",
       "      <th>Mechanism_(29, 162)</th>\n",
       "      <th>Mechanism_(29, 163)</th>\n",
       "      <th>Mechanism_(29, 179)</th>\n",
       "      <th>Mechanism_(29, 216)</th>\n",
       "      <th>Mechanism_(29, 217)</th>\n",
       "      <th>Mechanism_(29, 334)</th>\n",
       "      <th>Mechanism_(29, 382)</th>\n",
       "      <th>Mechanism_(29, 400)</th>\n",
       "      <th>Mechanism_(29, 448)</th>\n",
       "      <th>Mechanism_(32, 138)</th>\n",
       "      <th>Mechanism_(32, 170)</th>\n",
       "      <th>Mechanism_(32, 192)</th>\n",
       "      <th>Mechanism_(32, 224)</th>\n",
       "      <th>Mechanism_(33, 138)</th>\n",
       "      <th>Mechanism_(33, 139)</th>\n",
       "      <th>Mechanism_(33, 170)</th>\n",
       "      <th>Mechanism_(33, 171)</th>\n",
       "      <th>Mechanism_(33, 192)</th>\n",
       "      <th>Mechanism_(33, 193)</th>\n",
       "      <th>Mechanism_(33, 224)</th>\n",
       "      <th>Mechanism_(33, 225)</th>\n",
       "      <th>Mechanism_(37, 126)</th>\n",
       "      <th>Mechanism_(37, 162)</th>\n",
       "      <th>Mechanism_(37, 163)</th>\n",
       "      <th>Mechanism_(37, 179)</th>\n",
       "      <th>Mechanism_(37, 216)</th>\n",
       "      <th>Mechanism_(37, 217)</th>\n",
       "      <th>Mechanism_(37, 334)</th>\n",
       "      <th>Mechanism_(37, 382)</th>\n",
       "      <th>Mechanism_(37, 400)</th>\n",
       "      <th>Mechanism_(37, 448)</th>\n",
       "      <th>Mechanism_(4, 146)</th>\n",
       "      <th>Mechanism_(4, 147)</th>\n",
       "      <th>Mechanism_(4, 154)</th>\n",
       "      <th>Mechanism_(4, 155)</th>\n",
       "      <th>Mechanism_(40, 138)</th>\n",
       "      <th>Mechanism_(40, 170)</th>\n",
       "      <th>Mechanism_(40, 192)</th>\n",
       "      <th>Mechanism_(40, 224)</th>\n",
       "      <th>Mechanism_(41, 138)</th>\n",
       "      <th>Mechanism_(41, 139)</th>\n",
       "      <th>Mechanism_(41, 170)</th>\n",
       "      <th>Mechanism_(41, 171)</th>\n",
       "      <th>Mechanism_(41, 192)</th>\n",
       "      <th>Mechanism_(41, 193)</th>\n",
       "      <th>Mechanism_(41, 224)</th>\n",
       "      <th>Mechanism_(41, 225)</th>\n",
       "      <th>Mechanism_(42, 150)</th>\n",
       "      <th>Mechanism_(42, 151)</th>\n",
       "      <th>Mechanism_(42, 158)</th>\n",
       "      <th>Mechanism_(42, 159)</th>\n",
       "      <th>Mechanism_(43, 150)</th>\n",
       "      <th>Mechanism_(43, 151)</th>\n",
       "      <th>Mechanism_(43, 158)</th>\n",
       "      <th>Mechanism_(43, 159)</th>\n",
       "      <th>Mechanism_(44, 146)</th>\n",
       "      <th>Mechanism_(44, 147)</th>\n",
       "      <th>Mechanism_(44, 154)</th>\n",
       "      <th>Mechanism_(44, 155)</th>\n",
       "      <th>Mechanism_(44, 254)</th>\n",
       "      <th>Mechanism_(44, 255)</th>\n",
       "      <th>Mechanism_(44, 262)</th>\n",
       "      <th>Mechanism_(44, 263)</th>\n",
       "      <th>Mechanism_(45, 146)</th>\n",
       "      <th>Mechanism_(45, 147)</th>\n",
       "      <th>Mechanism_(45, 154)</th>\n",
       "      <th>Mechanism_(45, 155)</th>\n",
       "      <th>Mechanism_(45, 254)</th>\n",
       "      <th>Mechanism_(45, 255)</th>\n",
       "      <th>Mechanism_(45, 262)</th>\n",
       "      <th>Mechanism_(45, 263)</th>\n",
       "      <th>Mechanism_(5, 146)</th>\n",
       "      <th>Mechanism_(5, 147)</th>\n",
       "      <th>Mechanism_(5, 154)</th>\n",
       "      <th>Mechanism_(5, 155)</th>\n",
       "      <th>Mechanism_(50, 150)</th>\n",
       "      <th>Mechanism_(50, 151)</th>\n",
       "      <th>Mechanism_(50, 158)</th>\n",
       "      <th>Mechanism_(50, 159)</th>\n",
       "      <th>Mechanism_(51, 150)</th>\n",
       "      <th>Mechanism_(51, 151)</th>\n",
       "      <th>Mechanism_(51, 158)</th>\n",
       "      <th>Mechanism_(51, 159)</th>\n",
       "      <th>Mechanism_(52, 146)</th>\n",
       "      <th>Mechanism_(52, 147)</th>\n",
       "      <th>Mechanism_(52, 154)</th>\n",
       "      <th>Mechanism_(52, 155)</th>\n",
       "      <th>Mechanism_(52, 254)</th>\n",
       "      <th>Mechanism_(52, 255)</th>\n",
       "      <th>Mechanism_(52, 262)</th>\n",
       "      <th>Mechanism_(52, 263)</th>\n",
       "      <th>Mechanism_(53, 146)</th>\n",
       "      <th>Mechanism_(53, 147)</th>\n",
       "      <th>Mechanism_(53, 154)</th>\n",
       "      <th>Mechanism_(53, 155)</th>\n",
       "      <th>Mechanism_(53, 254)</th>\n",
       "      <th>Mechanism_(53, 255)</th>\n",
       "      <th>Mechanism_(53, 262)</th>\n",
       "      <th>Mechanism_(53, 263)</th>\n",
       "      <th>Mechanism_(55, 128)</th>\n",
       "      <th>Mechanism_(55, 166)</th>\n",
       "      <th>Mechanism_(55, 167)</th>\n",
       "      <th>Mechanism_(55, 338)</th>\n",
       "      <th>Mechanism_(55, 386)</th>\n",
       "      <th>Mechanism_(57, 126)</th>\n",
       "      <th>Mechanism_(57, 162)</th>\n",
       "      <th>Mechanism_(57, 163)</th>\n",
       "      <th>Mechanism_(57, 232)</th>\n",
       "      <th>Mechanism_(57, 270)</th>\n",
       "      <th>Mechanism_(57, 271)</th>\n",
       "      <th>Mechanism_(57, 334)</th>\n",
       "      <th>Mechanism_(57, 382)</th>\n",
       "      <th>Mechanism_(57, 466)</th>\n",
       "      <th>Mechanism_(57, 514)</th>\n",
       "      <th>Mechanism_(58, 142)</th>\n",
       "      <th>Mechanism_(58, 174)</th>\n",
       "      <th>Mechanism_(59, 142)</th>\n",
       "      <th>Mechanism_(59, 143)</th>\n",
       "      <th>Mechanism_(59, 174)</th>\n",
       "      <th>Mechanism_(59, 175)</th>\n",
       "      <th>Mechanism_(60, 138)</th>\n",
       "      <th>Mechanism_(60, 170)</th>\n",
       "      <th>Mechanism_(60, 246)</th>\n",
       "      <th>Mechanism_(60, 278)</th>\n",
       "      <th>Mechanism_(61, 138)</th>\n",
       "      <th>Mechanism_(61, 139)</th>\n",
       "      <th>Mechanism_(61, 170)</th>\n",
       "      <th>Mechanism_(61, 171)</th>\n",
       "      <th>Mechanism_(61, 246)</th>\n",
       "      <th>Mechanism_(61, 247)</th>\n",
       "      <th>Mechanism_(61, 278)</th>\n",
       "      <th>Mechanism_(61, 279)</th>\n",
       "      <th>Mechanism_(63, 128)</th>\n",
       "      <th>Mechanism_(63, 166)</th>\n",
       "      <th>Mechanism_(63, 167)</th>\n",
       "      <th>Mechanism_(63, 338)</th>\n",
       "      <th>Mechanism_(63, 386)</th>\n",
       "      <th>Mechanism_(65, 126)</th>\n",
       "      <th>Mechanism_(65, 162)</th>\n",
       "      <th>Mechanism_(65, 163)</th>\n",
       "      <th>Mechanism_(65, 232)</th>\n",
       "      <th>Mechanism_(65, 270)</th>\n",
       "      <th>Mechanism_(65, 271)</th>\n",
       "      <th>Mechanism_(65, 334)</th>\n",
       "      <th>Mechanism_(65, 382)</th>\n",
       "      <th>Mechanism_(65, 466)</th>\n",
       "      <th>Mechanism_(65, 514)</th>\n",
       "      <th>Mechanism_(66, 142)</th>\n",
       "      <th>Mechanism_(66, 174)</th>\n",
       "      <th>Mechanism_(67, 142)</th>\n",
       "      <th>Mechanism_(67, 143)</th>\n",
       "      <th>Mechanism_(67, 174)</th>\n",
       "      <th>Mechanism_(67, 175)</th>\n",
       "      <th>Mechanism_(68, 138)</th>\n",
       "      <th>Mechanism_(68, 170)</th>\n",
       "      <th>Mechanism_(68, 246)</th>\n",
       "      <th>Mechanism_(68, 278)</th>\n",
       "      <th>Mechanism_(69, 138)</th>\n",
       "      <th>Mechanism_(69, 139)</th>\n",
       "      <th>Mechanism_(69, 170)</th>\n",
       "      <th>Mechanism_(69, 171)</th>\n",
       "      <th>Mechanism_(69, 246)</th>\n",
       "      <th>Mechanism_(69, 247)</th>\n",
       "      <th>Mechanism_(69, 278)</th>\n",
       "      <th>Mechanism_(69, 279)</th>\n",
       "      <th>Mechanism_(7, 126)</th>\n",
       "      <th>Mechanism_(7, 162)</th>\n",
       "      <th>Mechanism_(7, 163)</th>\n",
       "      <th>Mechanism_(7, 334)</th>\n",
       "      <th>Mechanism_(7, 382)</th>\n",
       "      <th>Mechanism_(72, 150)</th>\n",
       "      <th>Mechanism_(72, 151)</th>\n",
       "      <th>Mechanism_(72, 158)</th>\n",
       "      <th>Mechanism_(72, 159)</th>\n",
       "      <th>Mechanism_(72, 204)</th>\n",
       "      <th>Mechanism_(72, 205)</th>\n",
       "      <th>Mechanism_(72, 212)</th>\n",
       "      <th>Mechanism_(72, 213)</th>\n",
       "      <th>Mechanism_(73, 150)</th>\n",
       "      <th>Mechanism_(73, 151)</th>\n",
       "      <th>Mechanism_(73, 158)</th>\n",
       "      <th>Mechanism_(73, 159)</th>\n",
       "      <th>Mechanism_(73, 204)</th>\n",
       "      <th>Mechanism_(73, 205)</th>\n",
       "      <th>Mechanism_(73, 212)</th>\n",
       "      <th>Mechanism_(73, 213)</th>\n",
       "      <th>Mechanism_(76, 146)</th>\n",
       "      <th>Mechanism_(76, 147)</th>\n",
       "      <th>Mechanism_(76, 154)</th>\n",
       "      <th>Mechanism_(76, 155)</th>\n",
       "      <th>Mechanism_(76, 200)</th>\n",
       "      <th>Mechanism_(76, 201)</th>\n",
       "      <th>Mechanism_(76, 208)</th>\n",
       "      <th>Mechanism_(76, 209)</th>\n",
       "      <th>Mechanism_(76, 254)</th>\n",
       "      <th>Mechanism_(76, 255)</th>\n",
       "      <th>Mechanism_(76, 262)</th>\n",
       "      <th>Mechanism_(76, 263)</th>\n",
       "      <th>Mechanism_(76, 308)</th>\n",
       "      <th>Mechanism_(76, 309)</th>\n",
       "      <th>Mechanism_(76, 316)</th>\n",
       "      <th>Mechanism_(76, 317)</th>\n",
       "      <th>Mechanism_(77, 146)</th>\n",
       "      <th>Mechanism_(77, 147)</th>\n",
       "      <th>Mechanism_(77, 154)</th>\n",
       "      <th>Mechanism_(77, 155)</th>\n",
       "      <th>Mechanism_(77, 200)</th>\n",
       "      <th>Mechanism_(77, 201)</th>\n",
       "      <th>Mechanism_(77, 208)</th>\n",
       "      <th>Mechanism_(77, 209)</th>\n",
       "      <th>Mechanism_(77, 254)</th>\n",
       "      <th>Mechanism_(77, 255)</th>\n",
       "      <th>Mechanism_(77, 262)</th>\n",
       "      <th>Mechanism_(77, 263)</th>\n",
       "      <th>Mechanism_(77, 308)</th>\n",
       "      <th>Mechanism_(77, 309)</th>\n",
       "      <th>Mechanism_(77, 316)</th>\n",
       "      <th>Mechanism_(77, 317)</th>\n",
       "      <th>Mechanism_(8, 138)</th>\n",
       "      <th>Mechanism_(8, 170)</th>\n",
       "      <th>Mechanism_(88, 150)</th>\n",
       "      <th>Mechanism_(88, 151)</th>\n",
       "      <th>Mechanism_(88, 158)</th>\n",
       "      <th>Mechanism_(88, 159)</th>\n",
       "      <th>Mechanism_(88, 204)</th>\n",
       "      <th>Mechanism_(88, 205)</th>\n",
       "      <th>Mechanism_(88, 212)</th>\n",
       "      <th>Mechanism_(88, 213)</th>\n",
       "      <th>Mechanism_(89, 150)</th>\n",
       "      <th>Mechanism_(89, 151)</th>\n",
       "      <th>Mechanism_(89, 158)</th>\n",
       "      <th>Mechanism_(89, 159)</th>\n",
       "      <th>Mechanism_(89, 204)</th>\n",
       "      <th>Mechanism_(89, 205)</th>\n",
       "      <th>Mechanism_(89, 212)</th>\n",
       "      <th>Mechanism_(89, 213)</th>\n",
       "      <th>Mechanism_(9, 138)</th>\n",
       "      <th>Mechanism_(9, 139)</th>\n",
       "      <th>Mechanism_(9, 170)</th>\n",
       "      <th>Mechanism_(9, 171)</th>\n",
       "      <th>Mechanism_(92, 146)</th>\n",
       "      <th>Mechanism_(92, 147)</th>\n",
       "      <th>Mechanism_(92, 154)</th>\n",
       "      <th>Mechanism_(92, 155)</th>\n",
       "      <th>Mechanism_(92, 200)</th>\n",
       "      <th>Mechanism_(92, 201)</th>\n",
       "      <th>Mechanism_(92, 208)</th>\n",
       "      <th>Mechanism_(92, 209)</th>\n",
       "      <th>Mechanism_(92, 254)</th>\n",
       "      <th>Mechanism_(92, 255)</th>\n",
       "      <th>Mechanism_(92, 262)</th>\n",
       "      <th>Mechanism_(92, 263)</th>\n",
       "      <th>Mechanism_(92, 308)</th>\n",
       "      <th>Mechanism_(92, 309)</th>\n",
       "      <th>Mechanism_(92, 316)</th>\n",
       "      <th>Mechanism_(92, 317)</th>\n",
       "      <th>Mechanism_(93, 146)</th>\n",
       "      <th>Mechanism_(93, 147)</th>\n",
       "      <th>Mechanism_(93, 154)</th>\n",
       "      <th>Mechanism_(93, 155)</th>\n",
       "      <th>Mechanism_(93, 200)</th>\n",
       "      <th>Mechanism_(93, 201)</th>\n",
       "      <th>Mechanism_(93, 208)</th>\n",
       "      <th>Mechanism_(93, 209)</th>\n",
       "      <th>Mechanism_(93, 254)</th>\n",
       "      <th>Mechanism_(93, 255)</th>\n",
       "      <th>Mechanism_(93, 262)</th>\n",
       "      <th>Mechanism_(93, 263)</th>\n",
       "      <th>Mechanism_(93, 308)</th>\n",
       "      <th>Mechanism_(93, 309)</th>\n",
       "      <th>Mechanism_(93, 316)</th>\n",
       "      <th>Mechanism_(93, 317)</th>\n",
       "      <th>Mechanism_(97, 128)</th>\n",
       "      <th>Mechanism_(97, 166)</th>\n",
       "      <th>Mechanism_(97, 167)</th>\n",
       "      <th>Mechanism_(97, 181)</th>\n",
       "      <th>Mechanism_(97, 220)</th>\n",
       "      <th>Mechanism_(97, 221)</th>\n",
       "      <th>Mechanism_(97, 338)</th>\n",
       "      <th>Mechanism_(97, 386)</th>\n",
       "      <th>Mechanism_(97, 404)</th>\n",
       "      <th>Mechanism_(97, 452)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1933581</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1933583</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Mechanism_(0, 146)  Mechanism_(0, 147)  Mechanism_(0, 154)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(0, 155)  Mechanism_(1, 146)  Mechanism_(1, 147)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(1, 154)  Mechanism_(1, 155)  Mechanism_(101, 126)  \\\n",
       "1933581               False               False                 False   \n",
       "1933582               False               False                 False   \n",
       "1933583               False               False                 False   \n",
       "1933584               False               False                 False   \n",
       "1933585               False               False                 False   \n",
       "\n",
       "         Mechanism_(101, 162)  Mechanism_(101, 163)  Mechanism_(101, 179)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 216)  Mechanism_(101, 217)  Mechanism_(101, 232)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 270)  Mechanism_(101, 271)  Mechanism_(101, 285)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 324)  Mechanism_(101, 325)  Mechanism_(101, 334)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 382)  Mechanism_(101, 400)  Mechanism_(101, 448)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 466)  Mechanism_(101, 514)  Mechanism_(101, 532)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(101, 580)  Mechanism_(104, 142)  Mechanism_(104, 174)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(104, 196)  Mechanism_(104, 228)  Mechanism_(105, 142)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(105, 143)  Mechanism_(105, 174)  Mechanism_(105, 175)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(105, 196)  Mechanism_(105, 197)  Mechanism_(105, 228)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(105, 229)  Mechanism_(108, 138)  Mechanism_(108, 170)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(108, 192)  Mechanism_(108, 224)  Mechanism_(108, 246)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(108, 278)  Mechanism_(108, 300)  Mechanism_(108, 332)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 138)  Mechanism_(109, 139)  Mechanism_(109, 170)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 171)  Mechanism_(109, 192)  Mechanism_(109, 193)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 224)  Mechanism_(109, 225)  Mechanism_(109, 246)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 247)  Mechanism_(109, 278)  Mechanism_(109, 279)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 300)  Mechanism_(109, 301)  Mechanism_(109, 332)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(109, 333)  Mechanism_(11, 126)  Mechanism_(11, 162)  \\\n",
       "1933581                 False                False                False   \n",
       "1933582                 False                False                False   \n",
       "1933583                 False                False                False   \n",
       "1933584                 False                False                False   \n",
       "1933585                 False                False                False   \n",
       "\n",
       "         Mechanism_(11, 163)  Mechanism_(11, 334)  Mechanism_(11, 382)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(113, 128)  Mechanism_(113, 166)  Mechanism_(113, 167)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(113, 181)  Mechanism_(113, 220)  Mechanism_(113, 221)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(113, 338)  Mechanism_(113, 386)  Mechanism_(113, 404)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(113, 452)  Mechanism_(117, 126)  Mechanism_(117, 162)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 163)  Mechanism_(117, 179)  Mechanism_(117, 216)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 217)  Mechanism_(117, 232)  Mechanism_(117, 270)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 271)  Mechanism_(117, 285)  Mechanism_(117, 324)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 325)  Mechanism_(117, 334)  Mechanism_(117, 382)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 400)  Mechanism_(117, 448)  Mechanism_(117, 466)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(117, 514)  Mechanism_(117, 532)  Mechanism_(117, 580)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(12, 138)  Mechanism_(12, 170)  Mechanism_(120, 142)  \\\n",
       "1933581                False                False                 False   \n",
       "1933582                False                False                 False   \n",
       "1933583                False                False                 False   \n",
       "1933584                False                False                 False   \n",
       "1933585                False                False                 False   \n",
       "\n",
       "         Mechanism_(120, 174)  Mechanism_(120, 196)  Mechanism_(120, 228)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(121, 142)  Mechanism_(121, 143)  Mechanism_(121, 174)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(121, 175)  Mechanism_(121, 196)  Mechanism_(121, 197)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(121, 228)  Mechanism_(121, 229)  Mechanism_(124, 138)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(124, 170)  Mechanism_(124, 192)  Mechanism_(124, 224)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(124, 246)  Mechanism_(124, 278)  Mechanism_(124, 300)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(124, 332)  Mechanism_(125, 138)  Mechanism_(125, 139)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(125, 170)  Mechanism_(125, 171)  Mechanism_(125, 192)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(125, 193)  Mechanism_(125, 224)  Mechanism_(125, 225)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(125, 246)  Mechanism_(125, 247)  Mechanism_(125, 278)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(125, 279)  Mechanism_(125, 300)  Mechanism_(125, 301)  \\\n",
       "1933581                 False                 False                 False   \n",
       "1933582                 False                 False                 False   \n",
       "1933583                 False                 False                 False   \n",
       "1933584                 False                 False                 False   \n",
       "1933585                 False                 False                 False   \n",
       "\n",
       "         Mechanism_(125, 332)  Mechanism_(125, 333)  Mechanism_(13, 138)  \\\n",
       "1933581                 False                 False                False   \n",
       "1933582                 False                 False                False   \n",
       "1933583                 False                 False                False   \n",
       "1933584                 False                 False                False   \n",
       "1933585                 False                 False                False   \n",
       "\n",
       "         Mechanism_(13, 139)  Mechanism_(13, 170)  Mechanism_(13, 171)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(16, 146)  Mechanism_(16, 147)  Mechanism_(16, 154)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(16, 155)  Mechanism_(16, 200)  Mechanism_(16, 201)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(16, 208)  Mechanism_(16, 209)  Mechanism_(17, 146)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(17, 147)  Mechanism_(17, 154)  Mechanism_(17, 155)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(17, 200)  Mechanism_(17, 201)  Mechanism_(17, 208)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(17, 209)  Mechanism_(24, 146)  Mechanism_(24, 147)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(24, 154)  Mechanism_(24, 155)  Mechanism_(24, 200)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(24, 201)  Mechanism_(24, 208)  Mechanism_(24, 209)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(25, 146)  Mechanism_(25, 147)  Mechanism_(25, 154)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(25, 155)  Mechanism_(25, 200)  Mechanism_(25, 201)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(25, 208)  Mechanism_(25, 209)  Mechanism_(29, 126)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(29, 162)  Mechanism_(29, 163)  Mechanism_(29, 179)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(29, 216)  Mechanism_(29, 217)  Mechanism_(29, 334)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(29, 382)  Mechanism_(29, 400)  Mechanism_(29, 448)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(32, 138)  Mechanism_(32, 170)  Mechanism_(32, 192)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(32, 224)  Mechanism_(33, 138)  Mechanism_(33, 139)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(33, 170)  Mechanism_(33, 171)  Mechanism_(33, 192)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(33, 193)  Mechanism_(33, 224)  Mechanism_(33, 225)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(37, 126)  Mechanism_(37, 162)  Mechanism_(37, 163)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(37, 179)  Mechanism_(37, 216)  Mechanism_(37, 217)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(37, 334)  Mechanism_(37, 382)  Mechanism_(37, 400)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(37, 448)  Mechanism_(4, 146)  Mechanism_(4, 147)  \\\n",
       "1933581                False               False               False   \n",
       "1933582                False               False               False   \n",
       "1933583                False               False               False   \n",
       "1933584                False               False               False   \n",
       "1933585                False               False               False   \n",
       "\n",
       "         Mechanism_(4, 154)  Mechanism_(4, 155)  Mechanism_(40, 138)  \\\n",
       "1933581               False               False                False   \n",
       "1933582               False               False                False   \n",
       "1933583               False               False                False   \n",
       "1933584               False               False                False   \n",
       "1933585               False               False                False   \n",
       "\n",
       "         Mechanism_(40, 170)  Mechanism_(40, 192)  Mechanism_(40, 224)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(41, 138)  Mechanism_(41, 139)  Mechanism_(41, 170)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(41, 171)  Mechanism_(41, 192)  Mechanism_(41, 193)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(41, 224)  Mechanism_(41, 225)  Mechanism_(42, 150)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(42, 151)  Mechanism_(42, 158)  Mechanism_(42, 159)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(43, 150)  Mechanism_(43, 151)  Mechanism_(43, 158)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(43, 159)  Mechanism_(44, 146)  Mechanism_(44, 147)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(44, 154)  Mechanism_(44, 155)  Mechanism_(44, 254)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(44, 255)  Mechanism_(44, 262)  Mechanism_(44, 263)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(45, 146)  Mechanism_(45, 147)  Mechanism_(45, 154)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(45, 155)  Mechanism_(45, 254)  Mechanism_(45, 255)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(45, 262)  Mechanism_(45, 263)  Mechanism_(5, 146)  \\\n",
       "1933581                False                False               False   \n",
       "1933582                False                False               False   \n",
       "1933583                False                False               False   \n",
       "1933584                False                False               False   \n",
       "1933585                False                False               False   \n",
       "\n",
       "         Mechanism_(5, 147)  Mechanism_(5, 154)  Mechanism_(5, 155)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(50, 150)  Mechanism_(50, 151)  Mechanism_(50, 158)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(50, 159)  Mechanism_(51, 150)  Mechanism_(51, 151)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(51, 158)  Mechanism_(51, 159)  Mechanism_(52, 146)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(52, 147)  Mechanism_(52, 154)  Mechanism_(52, 155)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(52, 254)  Mechanism_(52, 255)  Mechanism_(52, 262)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(52, 263)  Mechanism_(53, 146)  Mechanism_(53, 147)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(53, 154)  Mechanism_(53, 155)  Mechanism_(53, 254)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(53, 255)  Mechanism_(53, 262)  Mechanism_(53, 263)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(55, 128)  Mechanism_(55, 166)  Mechanism_(55, 167)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(55, 338)  Mechanism_(55, 386)  Mechanism_(57, 126)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(57, 162)  Mechanism_(57, 163)  Mechanism_(57, 232)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(57, 270)  Mechanism_(57, 271)  Mechanism_(57, 334)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(57, 382)  Mechanism_(57, 466)  Mechanism_(57, 514)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(58, 142)  Mechanism_(58, 174)  Mechanism_(59, 142)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(59, 143)  Mechanism_(59, 174)  Mechanism_(59, 175)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(60, 138)  Mechanism_(60, 170)  Mechanism_(60, 246)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(60, 278)  Mechanism_(61, 138)  Mechanism_(61, 139)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(61, 170)  Mechanism_(61, 171)  Mechanism_(61, 246)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(61, 247)  Mechanism_(61, 278)  Mechanism_(61, 279)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(63, 128)  Mechanism_(63, 166)  Mechanism_(63, 167)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(63, 338)  Mechanism_(63, 386)  Mechanism_(65, 126)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(65, 162)  Mechanism_(65, 163)  Mechanism_(65, 232)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(65, 270)  Mechanism_(65, 271)  Mechanism_(65, 334)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(65, 382)  Mechanism_(65, 466)  Mechanism_(65, 514)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(66, 142)  Mechanism_(66, 174)  Mechanism_(67, 142)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(67, 143)  Mechanism_(67, 174)  Mechanism_(67, 175)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(68, 138)  Mechanism_(68, 170)  Mechanism_(68, 246)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(68, 278)  Mechanism_(69, 138)  Mechanism_(69, 139)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(69, 170)  Mechanism_(69, 171)  Mechanism_(69, 246)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(69, 247)  Mechanism_(69, 278)  Mechanism_(69, 279)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(7, 126)  Mechanism_(7, 162)  Mechanism_(7, 163)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(7, 334)  Mechanism_(7, 382)  Mechanism_(72, 150)  \\\n",
       "1933581               False               False                False   \n",
       "1933582               False               False                False   \n",
       "1933583               False               False                False   \n",
       "1933584               False               False                False   \n",
       "1933585               False               False                False   \n",
       "\n",
       "         Mechanism_(72, 151)  Mechanism_(72, 158)  Mechanism_(72, 159)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(72, 204)  Mechanism_(72, 205)  Mechanism_(72, 212)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(72, 213)  Mechanism_(73, 150)  Mechanism_(73, 151)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(73, 158)  Mechanism_(73, 159)  Mechanism_(73, 204)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(73, 205)  Mechanism_(73, 212)  Mechanism_(73, 213)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 146)  Mechanism_(76, 147)  Mechanism_(76, 154)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 155)  Mechanism_(76, 200)  Mechanism_(76, 201)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 208)  Mechanism_(76, 209)  Mechanism_(76, 254)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 255)  Mechanism_(76, 262)  Mechanism_(76, 263)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 308)  Mechanism_(76, 309)  Mechanism_(76, 316)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(76, 317)  Mechanism_(77, 146)  Mechanism_(77, 147)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(77, 154)  Mechanism_(77, 155)  Mechanism_(77, 200)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(77, 201)  Mechanism_(77, 208)  Mechanism_(77, 209)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(77, 254)  Mechanism_(77, 255)  Mechanism_(77, 262)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(77, 263)  Mechanism_(77, 308)  Mechanism_(77, 309)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(77, 316)  Mechanism_(77, 317)  Mechanism_(8, 138)  \\\n",
       "1933581                False                False               False   \n",
       "1933582                False                False               False   \n",
       "1933583                False                False               False   \n",
       "1933584                False                False               False   \n",
       "1933585                False                False               False   \n",
       "\n",
       "         Mechanism_(8, 170)  Mechanism_(88, 150)  Mechanism_(88, 151)  \\\n",
       "1933581               False                False                False   \n",
       "1933582               False                False                False   \n",
       "1933583               False                False                False   \n",
       "1933584               False                False                False   \n",
       "1933585               False                False                False   \n",
       "\n",
       "         Mechanism_(88, 158)  Mechanism_(88, 159)  Mechanism_(88, 204)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(88, 205)  Mechanism_(88, 212)  Mechanism_(88, 213)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(89, 150)  Mechanism_(89, 151)  Mechanism_(89, 158)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(89, 159)  Mechanism_(89, 204)  Mechanism_(89, 205)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(89, 212)  Mechanism_(89, 213)  Mechanism_(9, 138)  \\\n",
       "1933581                False                False               False   \n",
       "1933582                False                False               False   \n",
       "1933583                False                False               False   \n",
       "1933584                False                False               False   \n",
       "1933585                False                False               False   \n",
       "\n",
       "         Mechanism_(9, 139)  Mechanism_(9, 170)  Mechanism_(9, 171)  \\\n",
       "1933581               False               False               False   \n",
       "1933582               False               False               False   \n",
       "1933583               False               False               False   \n",
       "1933584               False               False               False   \n",
       "1933585               False               False               False   \n",
       "\n",
       "         Mechanism_(92, 146)  Mechanism_(92, 147)  Mechanism_(92, 154)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(92, 155)  Mechanism_(92, 200)  Mechanism_(92, 201)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(92, 208)  Mechanism_(92, 209)  Mechanism_(92, 254)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(92, 255)  Mechanism_(92, 262)  Mechanism_(92, 263)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(92, 308)  Mechanism_(92, 309)  Mechanism_(92, 316)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(92, 317)  Mechanism_(93, 146)  Mechanism_(93, 147)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(93, 154)  Mechanism_(93, 155)  Mechanism_(93, 200)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(93, 201)  Mechanism_(93, 208)  Mechanism_(93, 209)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(93, 254)  Mechanism_(93, 255)  Mechanism_(93, 262)  \\\n",
       "1933581                 True                False                False   \n",
       "1933582                 True                False                False   \n",
       "1933583                 True                False                False   \n",
       "1933584                 True                False                False   \n",
       "1933585                 True                False                False   \n",
       "\n",
       "         Mechanism_(93, 263)  Mechanism_(93, 308)  Mechanism_(93, 309)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(93, 316)  Mechanism_(93, 317)  Mechanism_(97, 128)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 166)  Mechanism_(97, 167)  Mechanism_(97, 181)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 220)  Mechanism_(97, 221)  Mechanism_(97, 338)  \\\n",
       "1933581                False                False                False   \n",
       "1933582                False                False                False   \n",
       "1933583                False                False                False   \n",
       "1933584                False                False                False   \n",
       "1933585                False                False                False   \n",
       "\n",
       "         Mechanism_(97, 386)  Mechanism_(97, 404)  Mechanism_(97, 452)  \n",
       "1933581                False                False                False  \n",
       "1933582                False                False                False  \n",
       "1933583                False                False                False  \n",
       "1933584                False                False                False  \n",
       "1933585                False                False                False  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh = pd.get_dummies(y_df, columns = ['Mechanism']) \n",
    "y_df_oh.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = 'y_df_rate_oh.h5'\n",
    "y_df_oh.to_hdf(file_path, key='data', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1933586, 456)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_df_oh.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.where(y_df_oh, 1, 0)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_df, X_test_df, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.05, random_state=37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data has 1836906 observation with 240 features\n",
      "test data has 96680 observation with 240 features\n"
     ]
    }
   ],
   "source": [
    "print('training data has %d observation with %d features'% X_train_df.shape)\n",
    "print('test data has %d observation with %d features'% X_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cSM_0s</th>\n",
       "      <th>cSM_0.1s</th>\n",
       "      <th>cSM_1s</th>\n",
       "      <th>cSM_20s</th>\n",
       "      <th>cSM_40s</th>\n",
       "      <th>cSM_60s</th>\n",
       "      <th>cSM_120s</th>\n",
       "      <th>cSM_180s</th>\n",
       "      <th>cSM_240s</th>\n",
       "      <th>cSM_300s</th>\n",
       "      <th>cSM_360s</th>\n",
       "      <th>cSM_480s</th>\n",
       "      <th>cSM_600s</th>\n",
       "      <th>cSM_720s</th>\n",
       "      <th>cSM_840s</th>\n",
       "      <th>cSM_960s</th>\n",
       "      <th>cSM_1080s</th>\n",
       "      <th>cSM_1200s</th>\n",
       "      <th>cSM_1500s</th>\n",
       "      <th>cSM_1800s</th>\n",
       "      <th>cSM_2400s</th>\n",
       "      <th>cSM_3000s</th>\n",
       "      <th>cSM_3600s</th>\n",
       "      <th>cSM_4500s</th>\n",
       "      <th>cSM_5400s</th>\n",
       "      <th>cSM_6300s</th>\n",
       "      <th>cSM_7200s</th>\n",
       "      <th>cSM_9000s</th>\n",
       "      <th>cSM_10800s</th>\n",
       "      <th>cSM_14400s</th>\n",
       "      <th>cR_0s</th>\n",
       "      <th>cR_0.1s</th>\n",
       "      <th>cR_1s</th>\n",
       "      <th>cR_20s</th>\n",
       "      <th>cR_40s</th>\n",
       "      <th>cR_60s</th>\n",
       "      <th>cR_120s</th>\n",
       "      <th>cR_180s</th>\n",
       "      <th>cR_240s</th>\n",
       "      <th>cR_300s</th>\n",
       "      <th>cR_360s</th>\n",
       "      <th>cR_480s</th>\n",
       "      <th>cR_600s</th>\n",
       "      <th>cR_720s</th>\n",
       "      <th>cR_840s</th>\n",
       "      <th>cR_960s</th>\n",
       "      <th>cR_1080s</th>\n",
       "      <th>cR_1200s</th>\n",
       "      <th>cR_1500s</th>\n",
       "      <th>cR_1800s</th>\n",
       "      <th>cR_2400s</th>\n",
       "      <th>cR_3000s</th>\n",
       "      <th>cR_3600s</th>\n",
       "      <th>cR_4500s</th>\n",
       "      <th>cR_5400s</th>\n",
       "      <th>cR_6300s</th>\n",
       "      <th>cR_7200s</th>\n",
       "      <th>cR_9000s</th>\n",
       "      <th>cR_10800s</th>\n",
       "      <th>cR_14400s</th>\n",
       "      <th>cB_0s</th>\n",
       "      <th>cB_0.1s</th>\n",
       "      <th>cB_1s</th>\n",
       "      <th>cB_20s</th>\n",
       "      <th>cB_40s</th>\n",
       "      <th>cB_60s</th>\n",
       "      <th>cB_120s</th>\n",
       "      <th>cB_180s</th>\n",
       "      <th>cB_240s</th>\n",
       "      <th>cB_300s</th>\n",
       "      <th>cB_360s</th>\n",
       "      <th>cB_480s</th>\n",
       "      <th>cB_600s</th>\n",
       "      <th>cB_720s</th>\n",
       "      <th>cB_840s</th>\n",
       "      <th>cB_960s</th>\n",
       "      <th>cB_1080s</th>\n",
       "      <th>cB_1200s</th>\n",
       "      <th>cB_1500s</th>\n",
       "      <th>cB_1800s</th>\n",
       "      <th>cB_2400s</th>\n",
       "      <th>cB_3000s</th>\n",
       "      <th>cB_3600s</th>\n",
       "      <th>cB_4500s</th>\n",
       "      <th>cB_5400s</th>\n",
       "      <th>cB_6300s</th>\n",
       "      <th>cB_7200s</th>\n",
       "      <th>cB_9000s</th>\n",
       "      <th>cB_10800s</th>\n",
       "      <th>cB_14400s</th>\n",
       "      <th>cC_0s</th>\n",
       "      <th>cC_0.1s</th>\n",
       "      <th>cC_1s</th>\n",
       "      <th>cC_20s</th>\n",
       "      <th>cC_40s</th>\n",
       "      <th>cC_60s</th>\n",
       "      <th>cC_120s</th>\n",
       "      <th>cC_180s</th>\n",
       "      <th>cC_240s</th>\n",
       "      <th>cC_300s</th>\n",
       "      <th>cC_360s</th>\n",
       "      <th>cC_480s</th>\n",
       "      <th>cC_600s</th>\n",
       "      <th>cC_720s</th>\n",
       "      <th>cC_840s</th>\n",
       "      <th>cC_960s</th>\n",
       "      <th>cC_1080s</th>\n",
       "      <th>cC_1200s</th>\n",
       "      <th>cC_1500s</th>\n",
       "      <th>cC_1800s</th>\n",
       "      <th>cC_2400s</th>\n",
       "      <th>cC_3000s</th>\n",
       "      <th>cC_3600s</th>\n",
       "      <th>cC_4500s</th>\n",
       "      <th>cC_5400s</th>\n",
       "      <th>cC_6300s</th>\n",
       "      <th>cC_7200s</th>\n",
       "      <th>cC_9000s</th>\n",
       "      <th>cC_10800s</th>\n",
       "      <th>cC_14400s</th>\n",
       "      <th>cP_0s</th>\n",
       "      <th>cP_0.1s</th>\n",
       "      <th>cP_1s</th>\n",
       "      <th>cP_20s</th>\n",
       "      <th>cP_40s</th>\n",
       "      <th>cP_60s</th>\n",
       "      <th>cP_120s</th>\n",
       "      <th>cP_180s</th>\n",
       "      <th>cP_240s</th>\n",
       "      <th>cP_300s</th>\n",
       "      <th>cP_360s</th>\n",
       "      <th>cP_480s</th>\n",
       "      <th>cP_600s</th>\n",
       "      <th>cP_720s</th>\n",
       "      <th>cP_840s</th>\n",
       "      <th>cP_960s</th>\n",
       "      <th>cP_1080s</th>\n",
       "      <th>cP_1200s</th>\n",
       "      <th>cP_1500s</th>\n",
       "      <th>cP_1800s</th>\n",
       "      <th>cP_2400s</th>\n",
       "      <th>cP_3000s</th>\n",
       "      <th>cP_3600s</th>\n",
       "      <th>cP_4500s</th>\n",
       "      <th>cP_5400s</th>\n",
       "      <th>cP_6300s</th>\n",
       "      <th>cP_7200s</th>\n",
       "      <th>cP_9000s</th>\n",
       "      <th>cP_10800s</th>\n",
       "      <th>cP_14400s</th>\n",
       "      <th>cIMP1_0s</th>\n",
       "      <th>cIMP1_0.1s</th>\n",
       "      <th>cIMP1_1s</th>\n",
       "      <th>cIMP1_20s</th>\n",
       "      <th>cIMP1_40s</th>\n",
       "      <th>cIMP1_60s</th>\n",
       "      <th>cIMP1_120s</th>\n",
       "      <th>cIMP1_180s</th>\n",
       "      <th>cIMP1_240s</th>\n",
       "      <th>cIMP1_300s</th>\n",
       "      <th>cIMP1_360s</th>\n",
       "      <th>cIMP1_480s</th>\n",
       "      <th>cIMP1_600s</th>\n",
       "      <th>cIMP1_720s</th>\n",
       "      <th>cIMP1_840s</th>\n",
       "      <th>cIMP1_960s</th>\n",
       "      <th>cIMP1_1080s</th>\n",
       "      <th>cIMP1_1200s</th>\n",
       "      <th>cIMP1_1500s</th>\n",
       "      <th>cIMP1_1800s</th>\n",
       "      <th>cIMP1_2400s</th>\n",
       "      <th>cIMP1_3000s</th>\n",
       "      <th>cIMP1_3600s</th>\n",
       "      <th>cIMP1_4500s</th>\n",
       "      <th>cIMP1_5400s</th>\n",
       "      <th>cIMP1_6300s</th>\n",
       "      <th>cIMP1_7200s</th>\n",
       "      <th>cIMP1_9000s</th>\n",
       "      <th>cIMP1_10800s</th>\n",
       "      <th>cIMP1_14400s</th>\n",
       "      <th>cIMP2_0s</th>\n",
       "      <th>cIMP2_0.1s</th>\n",
       "      <th>cIMP2_1s</th>\n",
       "      <th>cIMP2_20s</th>\n",
       "      <th>cIMP2_40s</th>\n",
       "      <th>cIMP2_60s</th>\n",
       "      <th>cIMP2_120s</th>\n",
       "      <th>cIMP2_180s</th>\n",
       "      <th>cIMP2_240s</th>\n",
       "      <th>cIMP2_300s</th>\n",
       "      <th>cIMP2_360s</th>\n",
       "      <th>cIMP2_480s</th>\n",
       "      <th>cIMP2_600s</th>\n",
       "      <th>cIMP2_720s</th>\n",
       "      <th>cIMP2_840s</th>\n",
       "      <th>cIMP2_960s</th>\n",
       "      <th>cIMP2_1080s</th>\n",
       "      <th>cIMP2_1200s</th>\n",
       "      <th>cIMP2_1500s</th>\n",
       "      <th>cIMP2_1800s</th>\n",
       "      <th>cIMP2_2400s</th>\n",
       "      <th>cIMP2_3000s</th>\n",
       "      <th>cIMP2_3600s</th>\n",
       "      <th>cIMP2_4500s</th>\n",
       "      <th>cIMP2_5400s</th>\n",
       "      <th>cIMP2_6300s</th>\n",
       "      <th>cIMP2_7200s</th>\n",
       "      <th>cIMP2_9000s</th>\n",
       "      <th>cIMP2_10800s</th>\n",
       "      <th>cIMP2_14400s</th>\n",
       "      <th>cINT1_0s</th>\n",
       "      <th>cINT1_0.1s</th>\n",
       "      <th>cINT1_1s</th>\n",
       "      <th>cINT1_20s</th>\n",
       "      <th>cINT1_40s</th>\n",
       "      <th>cINT1_60s</th>\n",
       "      <th>cINT1_120s</th>\n",
       "      <th>cINT1_180s</th>\n",
       "      <th>cINT1_240s</th>\n",
       "      <th>cINT1_300s</th>\n",
       "      <th>cINT1_360s</th>\n",
       "      <th>cINT1_480s</th>\n",
       "      <th>cINT1_600s</th>\n",
       "      <th>cINT1_720s</th>\n",
       "      <th>cINT1_840s</th>\n",
       "      <th>cINT1_960s</th>\n",
       "      <th>cINT1_1080s</th>\n",
       "      <th>cINT1_1200s</th>\n",
       "      <th>cINT1_1500s</th>\n",
       "      <th>cINT1_1800s</th>\n",
       "      <th>cINT1_2400s</th>\n",
       "      <th>cINT1_3000s</th>\n",
       "      <th>cINT1_3600s</th>\n",
       "      <th>cINT1_4500s</th>\n",
       "      <th>cINT1_5400s</th>\n",
       "      <th>cINT1_6300s</th>\n",
       "      <th>cINT1_7200s</th>\n",
       "      <th>cINT1_9000s</th>\n",
       "      <th>cINT1_10800s</th>\n",
       "      <th>cINT1_14400s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459100</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.333312</td>\n",
       "      <td>0.333066</td>\n",
       "      <td>0.328139</td>\n",
       "      <td>0.323126</td>\n",
       "      <td>0.318280</td>\n",
       "      <td>0.304669</td>\n",
       "      <td>0.292298</td>\n",
       "      <td>0.280986</td>\n",
       "      <td>0.270614</td>\n",
       "      <td>0.261038</td>\n",
       "      <td>0.243972</td>\n",
       "      <td>0.229186</td>\n",
       "      <td>0.216212</td>\n",
       "      <td>0.204747</td>\n",
       "      <td>0.194524</td>\n",
       "      <td>0.185340</td>\n",
       "      <td>0.177048</td>\n",
       "      <td>0.159415</td>\n",
       "      <td>0.145150</td>\n",
       "      <td>0.123416</td>\n",
       "      <td>0.107571</td>\n",
       "      <td>0.095468</td>\n",
       "      <td>0.081828</td>\n",
       "      <td>0.071694</td>\n",
       "      <td>0.063846</td>\n",
       "      <td>0.057580</td>\n",
       "      <td>0.048207</td>\n",
       "      <td>0.041499</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>9.999909e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.469748e-05</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>2.597128e-03</td>\n",
       "      <td>5.103611e-03</td>\n",
       "      <td>7.526593e-03</td>\n",
       "      <td>0.014332</td>\n",
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       "      <td>0.597262</td>\n",
       "      <td>0.592670</td>\n",
       "      <td>0.588524</td>\n",
       "      <td>0.579707</td>\n",
       "      <td>0.572575</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1896046</th>\n",
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       "      <td>0.832626</td>\n",
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       "      <td>0.827164</td>\n",
       "      <td>0.825202</td>\n",
       "      <td>0.823285</td>\n",
       "      <td>0.821406</td>\n",
       "      <td>0.817763</td>\n",
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       "      <td>0.810927</td>\n",
       "      <td>0.807719</td>\n",
       "      <td>0.804639</td>\n",
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       "      <td>0.700771</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.895726</td>\n",
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       "      <td>0.876990</td>\n",
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       "    <tr>\n",
       "      <th>1574931</th>\n",
       "      <td>0.666667</td>\n",
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       "      <td>0.063048</td>\n",
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       "      <td>0.065211</td>\n",
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       "      <td>0.065278</td>\n",
       "      <td>0.065304</td>\n",
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       "      <td>0.065347</td>\n",
       "      <td>0.065355</td>\n",
       "      <td>0.065362</td>\n",
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       "      <td>0.065375</td>\n",
       "      <td>0.065381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cSM_0s  cSM_0.1s    cSM_1s   cSM_20s   cSM_40s   cSM_60s  cSM_120s  \\\n",
       "1459100  0.666666  0.000009  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.833333  0.833330  0.833298  0.832626  0.831924  0.831228  0.829172   \n",
       "968160   0.333333  0.333297  0.332970  0.326250  0.319520  0.313114  0.295610   \n",
       "605448   0.333333  0.003058  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.666667  0.666148  0.661630  0.603996  0.578012  0.563949  0.544214   \n",
       "\n",
       "         cSM_180s  cSM_240s  cSM_300s  cSM_360s  cSM_480s  cSM_600s  cSM_720s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.827164  0.825202  0.823285  0.821406  0.817763  0.814273  0.810927   \n",
       "968160   0.280275  0.266704  0.254590  0.243696  0.224858  0.209092  0.195665   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.535572  0.530625  0.527411  0.525170  0.522132  0.520222  0.518908   \n",
       "\n",
       "         cSM_840s  cSM_960s  cSM_1080s  cSM_1200s  cSM_1500s  cSM_1800s  \\\n",
       "1459100  0.000000  0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "1896046  0.807719  0.804639   0.801681   0.798837   0.792073   0.785874   \n",
       "968160   0.184066  0.173925   0.164971   0.156996   0.140371   0.127222   \n",
       "605448   0.000000  0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "1574931  0.517945  0.517145   0.516564   0.516164   0.515178   0.514545   \n",
       "\n",
       "         cSM_2400s  cSM_3000s  cSM_3600s  cSM_4500s  cSM_5400s  cSM_6300s  \\\n",
       "1459100   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "1896046   0.774952   0.765372   0.757043   0.746219   0.737015   0.729051   \n",
       "968160    0.107622   0.093610   0.083032   0.071207   0.062476   0.055740   \n",
       "605448    0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "1574931   0.513929   0.513447   0.513144   0.512799   0.512611   0.512461   \n",
       "\n",
       "         cSM_7200s  cSM_9000s  cSM_10800s  cSM_14400s  cR_0s   cR_0.1s  \\\n",
       "1459100   0.000000   0.000000    0.000000    0.000000    1.0  0.333312   \n",
       "1896046   0.722025   0.710316    0.700771    0.686122    1.0  0.999998   \n",
       "968160    0.050371   0.042325    0.036562    0.028821    0.4  0.399982   \n",
       "605448    0.000000   0.000000    0.000000    0.000000    0.0  0.000000   \n",
       "1574931   0.512332   0.512186    0.512072    0.511951    1.0  0.999482   \n",
       "\n",
       "            cR_1s    cR_20s    cR_40s    cR_60s   cR_120s   cR_180s   cR_240s  \\\n",
       "1459100  0.333066  0.328139  0.323126  0.318280  0.304669  0.292298  0.280986   \n",
       "1896046  0.999974  0.999303  0.998601  0.997905  0.995849  0.993841  0.991879   \n",
       "968160   0.399819  0.396459  0.393093  0.389890  0.381138  0.373471  0.366685   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.994951  0.923526  0.880320  0.855006  0.818143  0.801652  0.792148   \n",
       "\n",
       "          cR_300s   cR_360s   cR_480s   cR_600s   cR_720s   cR_840s   cR_960s  \\\n",
       "1459100  0.270614  0.261038  0.243972  0.229186  0.216212  0.204747  0.194524   \n",
       "1896046  0.989962  0.988082  0.984440  0.980949  0.977604  0.974395  0.971315   \n",
       "968160   0.360628  0.355181  0.345762  0.337879  0.331166  0.325366  0.320296   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.785949  0.781619  0.775738  0.772034  0.769484  0.767613  0.766059   \n",
       "\n",
       "         cR_1080s  cR_1200s  cR_1500s  cR_1800s  cR_2400s  cR_3000s  cR_3600s  \\\n",
       "1459100  0.185340  0.177048  0.159415  0.145150  0.123416  0.107571  0.095468   \n",
       "1896046  0.968357  0.965513  0.958749  0.952550  0.941628  0.932047  0.923718   \n",
       "968160   0.315819  0.311831  0.303519  0.296944  0.287144  0.280138  0.274849   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.764930  0.764150  0.762234  0.761002  0.759802  0.758864  0.758275   \n",
       "\n",
       "         cR_4500s  cR_5400s  cR_6300s  cR_7200s  cR_9000s  cR_10800s  \\\n",
       "1459100  0.081828  0.071694  0.063846  0.057580  0.048207   0.041499   \n",
       "1896046  0.912894  0.903689  0.895726  0.888699  0.876990   0.867445   \n",
       "968160   0.268937  0.264571  0.261203  0.258519  0.254496   0.251614   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000   0.000000   \n",
       "1574931  0.757601  0.757236  0.756944  0.756693  0.756408   0.756186   \n",
       "\n",
       "         cR_14400s  cB_0s   cB_0.1s     cB_1s    cB_20s    cB_40s    cB_60s  \\\n",
       "1459100   0.032520    0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046   0.852796    0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "968160    0.247744    1.0  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "605448    0.000000    1.0  0.669724  0.666667  0.666667  0.666667  0.666667   \n",
       "1574931   0.755950    0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          cB_120s   cB_180s   cB_240s   cB_300s   cB_360s   cB_480s   cB_600s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "968160   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "605448   0.666667  0.666667  0.666667  0.666667  0.666667  0.666667  0.666667   \n",
       "1574931  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          cB_720s   cB_840s   cB_960s  cB_1080s  cB_1200s  cB_1500s  cB_1800s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "968160   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "605448   0.666667  0.666667  0.666667  0.666667  0.666667  0.666667  0.666667   \n",
       "1574931  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "         cB_2400s  cB_3000s  cB_3600s  cB_4500s  cB_5400s  cB_6300s  cB_7200s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "968160   1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "605448   0.666667  0.666667  0.666667  0.666667  0.666667  0.666667  0.666667   \n",
       "1574931  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "         cB_9000s  cB_10800s  cB_14400s  cC_0s   cC_0.1s     cC_1s    cC_20s  \\\n",
       "1459100  0.000000   0.000000   0.000000   0.00  0.000000  0.000000  0.000000   \n",
       "1896046  0.000000   0.000000   0.000000   0.25  0.249998  0.249974  0.249303   \n",
       "968160   1.000000   1.000000   1.000000   0.10  0.100000  0.100000  0.100000   \n",
       "605448   0.666667   0.666667   0.666667   0.00  0.000000  0.000000  0.000000   \n",
       "1574931  0.000000   0.000000   0.000000   0.20  0.199482  0.194957  0.130427   \n",
       "\n",
       "           cC_40s    cC_60s   cC_120s   cC_180s   cC_240s   cC_300s   cC_360s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.248601  0.247905  0.245849  0.243841  0.241879  0.239962  0.238082   \n",
       "968160   0.100000  0.100000  0.100000  0.100000  0.100000  0.100000  0.100000   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.095833  0.076144  0.047846  0.035278  0.028053  0.023346  0.020061   \n",
       "\n",
       "          cC_480s   cC_600s   cC_720s   cC_840s   cC_960s  cC_1080s  cC_1200s  \\\n",
       "1459100  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.234440  0.230949  0.227604  0.224395  0.221315  0.218357  0.215513   \n",
       "968160   0.100000  0.100000  0.100000  0.100000  0.100000  0.100000  0.100000   \n",
       "605448   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.015602  0.012795  0.010862  0.009445  0.008269  0.007414  0.006824   \n",
       "\n",
       "         cC_1500s  cC_1800s  cC_2400s  cC_3000s  cC_3600s  cC_4500s  cC_5400s  \\\n",
       "1459100  0.000000   0.00000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1896046  0.208749   0.20255  0.191628  0.182047  0.173718  0.162894  0.153689   \n",
       "968160   0.100000   0.10000  0.100000  0.100000  0.100000  0.100000  0.100000   \n",
       "605448   0.000000   0.00000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1574931  0.005372   0.00444  0.003532  0.002822  0.002376  0.001867  0.001590   \n",
       "\n",
       "         cC_6300s  cC_7200s  cC_9000s  cC_10800s  cC_14400s     cP_0s  \\\n",
       "1459100  0.000000  0.000000  0.000000   0.000000   0.000000  0.333333   \n",
       "1896046  0.145726  0.138699  0.126990   0.117445   0.102796  0.416667   \n",
       "968160   0.100000  0.100000  0.100000   0.100000   0.100000  0.000000   \n",
       "605448   0.000000  0.000000  0.000000   0.000000   0.000000  0.166667   \n",
       "1574931  0.001370  0.001179  0.000964   0.000796   0.000617  0.000000   \n",
       "\n",
       "              cP_0.1s     cP_1s    cP_20s    cP_40s    cP_60s   cP_120s  \\\n",
       "1459100  9.999909e-01  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1896046  4.166668e-01  0.416675  0.417010  0.417361  0.417709  0.418737   \n",
       "968160   1.817036e-05  0.000181  0.003541  0.006907  0.010109  0.018856   \n",
       "605448   3.929313e-01  0.341316  0.333764  0.333543  0.333474  0.333402   \n",
       "1574931  6.094254e-09  0.000006  0.006902  0.015513  0.021138  0.029702   \n",
       "\n",
       "          cP_180s   cP_240s   cP_300s   cP_360s   cP_480s   cP_600s   cP_720s  \\\n",
       "1459100  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1896046  0.419741  0.420722  0.421681  0.422621  0.424442  0.426187  0.427860   \n",
       "968160   0.026505  0.033248  0.039230  0.044563  0.053612  0.060922  0.066856   \n",
       "605448   0.333380  0.333369  0.333360  0.333355  0.333351  0.333347  0.333345   \n",
       "1574931  0.033627  0.035905  0.037398  0.038442  0.039864  0.040761  0.041379   \n",
       "\n",
       "          cP_840s   cP_960s  cP_1080s  cP_1200s  cP_1500s  cP_1800s  cP_2400s  \\\n",
       "1459100  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1896046  0.429464  0.431004  0.432483  0.433905  0.437287  0.440387  0.445848   \n",
       "968160   0.071686  0.075622  0.078829  0.081442  0.086035  0.088756  0.091222   \n",
       "605448   0.333343  0.333342  0.333340  0.333340  0.333339  0.333338  0.333336   \n",
       "1574931  0.041833  0.042210  0.042484  0.042673  0.043139  0.043438  0.043730   \n",
       "\n",
       "         cP_3000s  cP_3600s  cP_4500s  cP_5400s  cP_6300s  cP_7200s  cP_9000s  \\\n",
       "1459100  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "1896046  0.450639  0.454803  0.460216  0.464818  0.468800  0.472313  0.478168   \n",
       "968160   0.091861  0.091797  0.091260  0.090610  0.089992  0.089443  0.088545   \n",
       "605448   0.333336  0.333335  0.333335  0.333335  0.333335  0.333334  0.333334   \n",
       "1574931  0.043958  0.044101  0.044265  0.044354  0.044425  0.044486  0.044556   \n",
       "\n",
       "         cP_10800s  cP_14400s  cIMP1_0s  cIMP1_0.1s  cIMP1_1s  cIMP1_20s  \\\n",
       "1459100   1.000000   1.000000  0.000000    0.000000  0.000000   0.000000   \n",
       "1896046   0.482941   0.490265  0.000000    0.000000  0.000000   0.000000   \n",
       "968160    0.087863   0.086912  0.000667    0.000685  0.000848   0.004208   \n",
       "605448    0.333334   0.333334  0.000000    0.330276  0.333333   0.333333   \n",
       "1574931   0.044610   0.044667  0.000000    0.000000  0.000000   0.000000   \n",
       "\n",
       "         cIMP1_40s  cIMP1_60s  cIMP1_120s  cIMP1_180s  cIMP1_240s  cIMP1_300s  \\\n",
       "1459100   0.000000   0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "1896046   0.000000   0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "968160    0.007573   0.010776    0.019528    0.027196    0.033981    0.040038   \n",
       "605448    0.333333   0.333333    0.333333    0.333333    0.333333    0.333333   \n",
       "1574931   0.000000   0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "         cIMP1_360s  cIMP1_480s  cIMP1_600s  cIMP1_720s  cIMP1_840s  \\\n",
       "1459100    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "1896046    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "968160     0.045485    0.054904    0.062787    0.069501    0.075301   \n",
       "605448     0.333333    0.333333    0.333333    0.333333    0.333333   \n",
       "1574931    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "         cIMP1_960s  cIMP1_1080s  cIMP1_1200s  cIMP1_1500s  cIMP1_1800s  \\\n",
       "1459100    0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "1896046    0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "968160     0.080371     0.084848     0.088835     0.097148     0.103722   \n",
       "605448     0.333333     0.333333     0.333333     0.333333     0.333333   \n",
       "1574931    0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "         cIMP1_2400s  cIMP1_3000s  cIMP1_3600s  cIMP1_4500s  cIMP1_5400s  \\\n",
       "1459100     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "1896046     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "968160      0.113522     0.120528     0.125818     0.131730     0.136095   \n",
       "605448      0.333333     0.333333     0.333333     0.333333     0.333333   \n",
       "1574931     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "         cIMP1_6300s  cIMP1_7200s  cIMP1_9000s  cIMP1_10800s  cIMP1_14400s  \\\n",
       "1459100     0.000000     0.000000     0.000000      0.000000      0.000000   \n",
       "1896046     0.000000     0.000000     0.000000      0.000000      0.000000   \n",
       "968160      0.139463     0.142148     0.146171      0.149052      0.152923   \n",
       "605448      0.333333     0.333333     0.333333      0.333333      0.333333   \n",
       "1574931     0.000000     0.000000     0.000000      0.000000      0.000000   \n",
       "\n",
       "         cIMP2_0s    cIMP2_0.1s  cIMP2_1s     cIMP2_20s     cIMP2_40s  \\\n",
       "1459100       0.0  1.469748e-05  0.000133  2.597128e-03  5.103611e-03   \n",
       "1896046       0.0  1.432294e-07  0.000008  3.433804e-04  6.941864e-04   \n",
       "968160        0.0  0.000000e+00  0.000000  4.946401e-09  8.717324e-08   \n",
       "605448        0.0  1.040109e-01  0.158684  1.662364e-01  1.664573e-01   \n",
       "1574931       0.0  0.000000e+00  0.000000  0.000000e+00  0.000000e+00   \n",
       "\n",
       "            cIMP2_60s  cIMP2_120s  cIMP2_180s  cIMP2_240s  cIMP2_300s  \\\n",
       "1459100  7.526593e-03    0.014332    0.020518    0.026174    0.031360   \n",
       "1896046  1.042407e-03    0.002070    0.003074    0.004055    0.005014   \n",
       "968160   4.204154e-07    0.000006    0.000025    0.000067    0.000142   \n",
       "605448   1.665258e-01    0.166598    0.166620    0.166631    0.166640   \n",
       "1574931  0.000000e+00    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "         cIMP2_360s  cIMP2_480s  cIMP2_600s  cIMP2_720s  cIMP2_840s  \\\n",
       "1459100    0.036148    0.044680    0.052073    0.058561    0.064293   \n",
       "1896046    0.005954    0.007775    0.009521    0.011193    0.012798   \n",
       "968160     0.000256    0.000625    0.001199    0.001978    0.002947   \n",
       "605448     0.166645    0.166649    0.166653    0.166655    0.166657   \n",
       "1574931    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "\n",
       "         cIMP2_960s  cIMP2_1080s  cIMP2_1200s  cIMP2_1500s  cIMP2_1800s  \\\n",
       "1459100    0.069405     0.073996     0.078142     0.086959     0.094092   \n",
       "1896046    0.014338     0.015817     0.017239     0.020621     0.023720   \n",
       "968160     0.004082     0.005352     0.006727     0.010446     0.014300   \n",
       "605448     0.166658     0.166660     0.166660     0.166661     0.166662   \n",
       "1574931    0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "         cIMP2_2400s  cIMP2_3000s  cIMP2_3600s  cIMP2_4500s  cIMP2_5400s  \\\n",
       "1459100     0.104959     0.112881     0.118933     0.125752     0.130819   \n",
       "1896046     0.029182     0.033972     0.038137     0.043549     0.048151   \n",
       "968160      0.021634     0.028000     0.033354     0.039803     0.044819   \n",
       "605448      0.166664     0.166664     0.166665     0.166665     0.166665   \n",
       "1574931     0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "\n",
       "         cIMP2_6300s  cIMP2_7200s  cIMP2_9000s  cIMP2_10800s  cIMP2_14400s  \\\n",
       "1459100     0.134744     0.137876     0.142563      0.145917      0.150406   \n",
       "1896046     0.052133     0.055647     0.061501      0.066274      0.073599   \n",
       "968160      0.048804     0.052038     0.056959      0.060523      0.065344   \n",
       "605448      0.166665     0.166666     0.166666      0.166666      0.166666   \n",
       "1574931     0.000000     0.000000     0.000000      0.000000      0.000000   \n",
       "\n",
       "         cINT1_0s  cINT1_0.1s  cINT1_1s  cINT1_20s  cINT1_40s  cINT1_60s  \\\n",
       "1459100       0.0    0.666643  0.666533   0.664069   0.661563   0.659140   \n",
       "1896046       0.0    0.000002  0.000010   0.000010   0.000010   0.000010   \n",
       "968160        0.0    0.000018  0.000181   0.003541   0.006907   0.010109   \n",
       "605448        0.0    0.122254  0.015965   0.000861   0.000419   0.000282   \n",
       "1574931       0.0    0.000518  0.005024   0.048868   0.057629   0.060441   \n",
       "\n",
       "         cINT1_120s  cINT1_180s  cINT1_240s  cINT1_300s  cINT1_360s  \\\n",
       "1459100    0.652334    0.646149    0.640493    0.635307    0.630519   \n",
       "1896046    0.000010    0.000010    0.000010    0.000010    0.000010   \n",
       "968160     0.018850    0.026480    0.033181    0.039089    0.044307   \n",
       "605448     0.000137    0.000092    0.000071    0.000054    0.000044   \n",
       "1574931    0.063048    0.063842    0.064231    0.064461    0.064612   \n",
       "\n",
       "         cINT1_480s  cINT1_600s  cINT1_720s  cINT1_840s  cINT1_960s  \\\n",
       "1459100    0.621986    0.614593    0.608106    0.602373    0.597262   \n",
       "1896046    0.000010    0.000010    0.000010    0.000010    0.000009   \n",
       "968160     0.052987    0.059723    0.064878    0.068739    0.071540   \n",
       "605448     0.000035    0.000027    0.000024    0.000020    0.000017   \n",
       "1574931    0.064807    0.064924    0.065001    0.065057    0.065103   \n",
       "\n",
       "         cINT1_1080s  cINT1_1200s  cINT1_1500s  cINT1_1800s  cINT1_2400s  \\\n",
       "1459100     0.592670     0.588524     0.579707     0.572575     0.561708   \n",
       "1896046     0.000009     0.000009     0.000009     0.000009     0.000009   \n",
       "968160      0.073477     0.074714     0.075589     0.074456     0.069588   \n",
       "605448      0.000014     0.000013     0.000011     0.000009     0.000006   \n",
       "1574931     0.065135     0.065157     0.065211     0.065246     0.065278   \n",
       "\n",
       "         cINT1_3000s  cINT1_3600s  cINT1_4500s  cINT1_5400s  cINT1_6300s  \\\n",
       "1459100     0.553785     0.547734     0.540914     0.535847     0.531923   \n",
       "1896046     0.000009     0.000008     0.000008     0.000008     0.000008   \n",
       "968160      0.063861     0.058443     0.051457     0.045791     0.041188   \n",
       "605448      0.000006     0.000004     0.000003     0.000003     0.000003   \n",
       "1574931     0.065304     0.065319     0.065338     0.065347     0.065355   \n",
       "\n",
       "         cINT1_7200s  cINT1_9000s  cINT1_10800s  cINT1_14400s  \n",
       "1459100     0.528790     0.524104      0.520750      0.516260  \n",
       "1896046     0.000008     0.000007      0.000007      0.000007  \n",
       "968160      0.037404     0.031587      0.027340      0.021568  \n",
       "605448      0.000002     0.000002      0.000001      0.000001  \n",
       "1574931     0.065362     0.065369      0.065375      0.065381  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.66666375e-01, 8.64659390e-06, 0.00000000e+00, ...,\n",
       "        5.24103567e-01, 5.20749569e-01, 5.16260321e-01],\n",
       "       [8.33333333e-01, 8.33329783e-01, 8.33297833e-01, ...,\n",
       "        7.39333964e-06, 7.19593722e-06, 6.89835126e-06],\n",
       "       [3.33333333e-01, 3.33296993e-01, 3.32970356e-01, ...,\n",
       "        3.15867918e-02, 2.73400487e-02, 2.15682561e-02],\n",
       "       ...,\n",
       "       [3.33333333e-01, 3.33324903e-01, 3.33249064e-01, ...,\n",
       "        8.90973969e-02, 9.32020928e-02, 9.88450912e-02],\n",
       "       [3.33333333e-01, 3.33322224e-01, 3.33222288e-01, ...,\n",
       "        1.10472690e-01, 1.14783402e-01, 1.20264515e-01],\n",
       "       [3.33333333e-01, 3.31398102e-01, 3.18951029e-01, ...,\n",
       "        7.26743944e-06, 6.06035491e-06, 4.55024782e-06]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train_df.to_numpy()\n",
    "X_test = X_test_df.to_numpy()\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[412 245 128 ...  62 136 400]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_train = np.argmax(y_train, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_train_1D = indices_train + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[306 402  60 ... 131 132 154]\n"
     ]
    }
   ],
   "source": [
    "# Find the indices of '1' in each row\n",
    "indices_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "# Create a 1D array with values corresponding to the positions of '1'\n",
    "y_test_1D = indices_test + 1  # Adding 1 to make the index 1-based\n",
    "\n",
    "# Print the result\n",
    "print(y_test_1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.2 Train RF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_RF_120 = RandomForestClassifier(n_estimators=120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=120)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=120)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=120)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_RF_120.fit(X_train, y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9996249127609143"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_RF_120.score(X_train,y_train_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_predict_1D_120 = classifier_RF_120.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5241208109226314"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_RF_120.score(X_test,y_test_1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "joblib.dump(classifier_RF_120, \"Step 3_RF_rate_n_estimators=120.joblib\", compress=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_predict_1D_120 = classifier_RF_120.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data\n",
    "plt.scatter(y_test_1D, RF_predict_1D_120, marker='o', s = 1, label='Data Points')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_predicted')\n",
    "plt.title('RF_Rate_120')\n",
    "\n",
    "# # Adding a legend\n",
    "# plt.legend()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_predicted_grouping(predictions):\n",
    "    threshold = 1.0\n",
    "    index = np.argsort(predictions) + 1\n",
    "    prob = 0\n",
    "    grouping = []\n",
    "    probabilities = []\n",
    "    for j in index[::-1]:\n",
    "        prob += predictions[j - 1]\n",
    "        grouping.append(j)\n",
    "        probabilities.append(predictions[j - 1])\n",
    "        if prob >= threshold:\n",
    "            break\n",
    "    # print(grouping)\n",
    "    # print(probabilities)\n",
    "    return grouping, probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_proba_120 = classifier_RF_120.predict_proba(X_test)\n",
    "print(y_predicted_proba_120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage: 99.67107985105503\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "total_iterations = len(y_predicted_proba_120)\n",
    "for idx_proba, i in enumerate(y_predicted_proba_120):\n",
    "    # print(idx_proba)\n",
    "    grouping, probabilities = generate_predicted_grouping(i)\n",
    "    if y_test_1D[idx_proba] in grouping:\n",
    "        cnt += 1\n",
    "\n",
    "percentage = (cnt / total_iterations) * 100\n",
    "print(\"Percentage:\", percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the percentage of correct predictions for each unique pair of true and predicted values\n",
    "count_dict = {} \n",
    "for true_val, pred_val in zip(y_test_1D, RF_predict_1D_120):\n",
    "    if (true_val, pred_val) not in count_dict:\n",
    "        count_dict[(true_val, pred_val)] = 1\n",
    "    else:\n",
    "        count_dict[(true_val, pred_val)] += 1\n",
    "# print(count_dict)\n",
    "\n",
    "# Calculate total counts for each value starting from 1 to 456 for key[0]\n",
    "total_counts = {}\n",
    "for i in range(1, 457):\n",
    "    total_counts[i] = sum(value for key, value in count_dict.items() if key[0] == i)\n",
    "# print(total_counts)\n",
    "\n",
    "\n",
    "# Calculate percentages\n",
    "percentages = {}\n",
    "for key, value in count_dict.items():\n",
    "    # print(key)\n",
    "    # print(value)\n",
    "    percentages[key] = (value / total_counts[key[0]]) * 100\n",
    "# print(percentages)\n",
    "\n",
    "# Extract x and y data for the scatter plot\n",
    "x_data = [key[0] for key in percentages]\n",
    "y_data = [key[1] for key in percentages]\n",
    "colors = [value for value in percentages.values()]\n",
    "\n",
    "plt.figure(figsize=(11, 8))  # Adjust width and height as needed\n",
    "\n",
    "# Create the scatter plot\n",
    "# plt.scatter(x_data, y_data, c=colors, cmap='viridis', s = 1)\n",
    "plt.scatter(x_data, y_data, c=colors, cmap='Reds', s = 3) #, zorder=1)\n",
    "\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Label of True Mechanism', fontsize = 24, labelpad=15)\n",
    "plt.ylabel('Label of Predicted Mechanism', fontsize = 24, labelpad=15)\n",
    "# plt.title('Scatter Plot')\n",
    "\n",
    "# Add color bar\n",
    "cbar = plt.colorbar(ticks=[0, 20, 40, 60, 80, 100])\n",
    "cbar.set_label('Percentage', fontsize=18)  # Adjust the fontsize and labelpad as needed\n",
    "# Set font size of ticks in color bar\n",
    "cbar.ax.tick_params(labelsize=16)  # Adjust the font size as needed\n",
    "\n",
    "# Add axis ticks\n",
    "plt.xticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.yticks([1, 100, 200, 300, 400, 457], fontsize = 20)  # Adjust the range and step size for x-axis ticks\n",
    "plt.tick_params(axis='both', direction='in')  # Set ticks to be inside the plot\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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